Skip to main content
Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

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

By Felipe Flores

Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace.
As more and more organisations adopt and implement AI we find that the main challenges are not in the technology itself but in the human side, ie: the approaches, chosen problems and what's called 'the last mile', etc.

That's why Data Futurology focuses on the leadership side of AI and how to get the most value from it.

Join me, Felipe Flores, a Data Science executive with almost 20 years of experience in the space. Every week I speak with top industry leaders from around the world
Available on
Apple Podcasts Logo
Castbox Logo
Google Podcasts Logo
Overcast Logo
Pocket Casts Logo
PodBean Logo
RadioPublic Logo
Spotify Logo
TuneIn Logo
Currently playing episode

#19 Vlad Kazantsev - Head of Data Science

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data ScienceSep 05, 2018

00:00
58:17
#249 - Generative AI in Recruitment: Bridging the Gap Between Automation and Authenticity

#249 - Generative AI in Recruitment: Bridging the Gap Between Automation and Authenticity

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.

Oct 25, 202336:30
#248: Navigating the Frontier of Generative AI in Business

#248: Navigating the Frontier of Generative AI in Business

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.

Oct 23, 202339:14
#247: Navigating the Ethical Waters of Data and AI - Insights from NAB's Head of Privacy and Data Ethics

#247: Navigating the Ethical Waters of Data and AI - Insights from NAB's Head of Privacy and Data Ethics

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.

Oct 04, 202346:40
#246: Unlocking Value with Generative AI

#246: Unlocking Value with Generative AI

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. 

Sep 27, 202344:15
#245 - Becoming a Successful Data Analytics and AI Leader

#245 - Becoming a Successful Data Analytics and AI Leader

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.


Sep 20, 202345:38
#244: Navigating Data Quality: Insights from the Chief Operator of Data Quality Camp

#244: Navigating Data Quality: Insights from the Chief Operator of Data Quality Camp

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

Aug 16, 202338:52
#243 Mastering DataOps and MLOps: Building a Strong Foundation for Success and Future Growth

#243 Mastering DataOps and MLOps: Building a Strong Foundation for Success and Future Growth

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?

 


Aug 08, 202339:02
#242: Tell me about the future of AI… Here Be Dragons?

#242: Tell me about the future of AI… Here Be Dragons?

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.

Aug 02, 202337:07
#241 - Building AI systems with quality, holistic data

#241 - Building AI systems with quality, holistic data

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.

Jul 19, 202329:56
#240: Overcoming the challenges facing modern data engineering teams

#240: Overcoming the challenges facing modern data engineering teams

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.

Jul 12, 202343:14
#239: Building better business culture around AI

#239: Building better business culture around AI

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

Jul 04, 202336:08
 #238: Transforming Education with AI Advancements with Alex Jenkins

#238: Transforming Education with AI Advancements with Alex Jenkins

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

Jun 28, 202342:31
#237: Evolving data culture to deliver sustainable business impact, with Niall Keating, General Manager for Technology Data Platforms, Sportsbet

#237: Evolving data culture to deliver sustainable business impact, with Niall Keating, General Manager for Technology Data Platforms, Sportsbet

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

Jun 21, 202327:08
#236: Building ML Products at Compare the Market, with Conor O'Neill, the Head of Data Science at Compare The Market

#236: Building ML Products at Compare the Market, with Conor O'Neill, the Head of Data Science at Compare The Market

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.”

Jun 13, 202352:34
#235: Maximising the productivity of the data-led enterprise with UNSW, EG Australia and Compare the Market

#235: Maximising the productivity of the data-led enterprise with UNSW, EG Australia and Compare the Market

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?”

Jun 07, 202339:53
#234: Innovating with Data in Healthcare: Part Two

#234: Innovating with Data in Healthcare: Part Two

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.”

May 31, 202343:15
#233: Innovating with Data: Part One, with the Head of Data Science at Maurice Blackburn Lawyers, Jonas Christensen

#233: Innovating with Data: Part One, with the Head of Data Science at Maurice Blackburn Lawyers, Jonas Christensen

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

May 25, 202353:22
#232: Getting buy in and investment from senior execs for your data & AI projects, with Brian Ferris, Chief Data, Analytics and Technology Officer at Loyalty New Zealand

#232: Getting buy in and investment from senior execs for your data & AI projects, with Brian Ferris, Chief Data, Analytics and Technology Officer at Loyalty New Zealand

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).

May 17, 202343:04
#231: Revolutionising Property Technology with Modular Analytics, with General Manager, Innovation & Advanced Analytics of Investa Property Group

#231: Revolutionising Property Technology with Modular Analytics, with General Manager, Innovation & Advanced Analytics of Investa Property Group

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?

May 10, 202352:13
#230: From Walmart to ASB Bank: Achieving some of the largest data transformations in the world, with Bora Arslan

#230: From Walmart to ASB Bank: Achieving some of the largest data transformations in the world, with Bora Arslan

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.

Apr 19, 202342:10
#229: Modernising the ATO to drive cloud data capabilities, analytics, AI, and deliver innovation. With Ben Taylor, the Assistant Commissioner for Data Insights at the ATO

#229: Modernising the ATO to drive cloud data capabilities, analytics, AI, and deliver innovation. With Ben Taylor, the Assistant Commissioner for Data Insights at the ATO

This week in the Data Futurology podcast, we have a special presentation to share. Ben Taylor, the Assistant Commissioner for Data Insights at the ATO was one of the leading keynote speakers at our recent OpsWorld event in Sydney. There, he provided delegates with a deep dive into the data journey for the ATO in recent years. 

As you can probably guess, the ATO handles millions of lines of data every day, across data lakes that are petabytes in size. With a data team of around 800, there is often the sense that they’re racing against chaos to deliver. However, in recent years, the effort to transform and modernise the approach to data has been highly successful. The ATO was able to transition to cloud-driven data systems, and is now seen as a deep and strategic partner to the other lines of business within the organisation. 

In this presentation, Taylor shares some open and transparent examples of the challenges that the ATO faced, the steps that they took to embrace AI and automated analytics while maintaining human oversight and decision-making, and how the data team went about building trust to earn the support of the other lines of business. 

He also overviews the value of XOps – what that means from the ATO’s perspective – and why all data leaders should be looking at defining and adopting a XOps approach to their own data strategy. 

For deep insights into one of the largest data-driven organisations in Australia, Taylor’s presentation on the ATO’s experience with data is essential.

Connect with Ben Taylor:  https://www.linkedin.com/in/ben-taylor-962a2a60/?originalSubdomain=au


Joins 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

2:17: Introduction to Ben Taylor’s presentation.

7:06: Taylor introduces himself and provides the historical context for the ATO’s approach to analytics.

12:53: Taylor describes the state of the ATO’s data environment eight years ago, and overviews the transformation project to modernise it.

14:24: Taylor describes some of the challenges that the ATO found with centralising the data and analytics function.

16:42: XOps in the ATO – what challenges led to the ATO approaching data this way, and what impact did it have?

18:03: What, exactly, does “XOps” mean to the ATO? Ben shares his insights on the conversation. 

19:27: Taylor shares an example of what XOps looks like in action at the ATO.

23:45: How did the ATO avoid becoming too process heavy, as Government agencies can at times become?

27:13: How can teams handle the sense of “chaos” that comes from increasing demands from lines of business, while also managing the legacy tech debt?

29:13: Q & A with Taylor from the audience.

EPISODE HIGHLIGHTS

  • “Since the earliest examples of tabular data structures two and a half 1000 years ago to the earliest examples of statistical data and analytics about 350 years ago, the pace of human abilities in regards to data analysis has increased at an incredibly, incredibly fast pace.”

  • “The real tipping point for digitally enabled data and analytics came a mere 27 years ago when for the first time, the cost of storing information on digital media dropped below that of storage on paper.”

    “At the ATO, we see data as the third note of a triad between business, technology, and data.

    “What exactly is XOps? Honestly, we've spent a lot of time asking ourselves the same question. If you go out there and try to find someone who can tell you what XOps is, you won't find it… although I'm sure you'll find a few consultants that will sell you an answer for a few $100,000.”

Apr 13, 202330:33
#228 Next generation technology and its impact on the way you work. With Nikita Atkins, the Artificial Intelligence Executive at NCS Australia.

#228 Next generation technology and its impact on the way you work. With Nikita Atkins, the Artificial Intelligence Executive at NCS Australia.

The future of AI is dynamic and multi-faceted. In this episode of the Data Futurology podcast, we are thrilled to welcome Nikita Atkins, the Artificial Intelligence Executive at NCS Australia. With NCS being one of the leading voices in AI, both in the APAC region and globally, Nikita has more than a few insights to share about the future of the technology and its most exciting use cases. 

We start by talking about low code/no code and how, by embracing that and enhancing it with AI, an organisation can shift their data science team away from “run-rate” models and tasks to instead focus on the highest value items. 

From there, we talk about data cleaning and pipelines, before moving on to some of the exciting innovations that are coming to the AI space – how will AI assist in rebuilding digital trust after so many high-profile cyber breaches have shaken the confidence of Australian consumers? How can AI play a role in enhancing the sustainability credentials of organisations? And what are new concepts like AI ops and Explainable AI, and how is NCS set up to be a pioneer in this space?

This is a far-reaching and in-depth interview, you’ll get a good sense of how organisations will be transforming their AI environments in the years ahead.

Don’t forget!  NCS will be at the Data Futurology Advancing AI conference in Melbourne in May. Be sure to come up and speak to Nikita and his team!

Connect with Nikita: https://www.linkedin.com/in/nikitaatkins/

Learn more about NCS: https://www.ncs.co

See NCS 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

00:00 Introducing Nikita Atkins and the topics for the podcast.

1:04 Nikita’s background, his role at NCS, and a company overview.

4:16 On the topic of generative AI – what’s behind the interest and excitement in this area?

5:43 How generative AI tools can be effectively used in the enterprise.

9:46 On the subject of AI and low code/no code – how can organisations implement AI in a way that can enhance this area?

12:20 What should organisations be thinking about in terms of governance or deployment challenges with regard to low code/no code?

15:20 In terms of data cleansing, do we get better outcomes from better quality data and better structured model data?

18:36 Data pipelines are a critical need for any business working with data – what role does automation have to play?

23:15 The advantages of standardising data collection.

25:56 The emergence of and benefits behind AI ops.

29:36 NCS and sustainability – how can data be part of the solution?

35:17 Digital trust – in the wake of so many cyber breaches, what can enterprises do to earn the respect of customers back?

38:05 The concept of Explainable AI – what is it, and why is it a focus for NCS?

EPISODE HIGHLIGHTS

  • “One of the key things that we see more, particularly those organisations that are very mature in data science, is that they are still making interesting choices, where data scientists still collect the same raw data in different ways. They're still cleaning it in different ways. And then they're doing ML. What we’re looking at is whether we can actually automate that process.”

  • “80% of scientists will admit to you that they don't like doing data cleansing. Well, let's automate that, standardise that and let them do what they do best."

  • “Some of our big clients have excellent science teams. But the problem is data scientists are not the cheapest people resources around. So a lot of organisations may have 10, 15, and perhaps as many as 50 data scientists. But if you take the power of low code, and you give that to the broader business, then you're unlocking the power of numbers.”

Apr 05, 202348:38
#226: Strategies for Strengthening Data Team Relationships with the Organisation, with Sandra Hogan Data Science & Analytics Leader and Co-founder, Amperfii

#226: Strategies for Strengthening Data Team Relationships with the Organisation, with Sandra Hogan Data Science & Analytics Leader and Co-founder, Amperfii

This week’s guest is a true veteran of the data industry (and one of the first people we interviewed on the Data Futurology podcast!). Sandra Hogan has some of the deepest and most experienced views on how data teams can effectively engage with their organisations. Fifteen years ago, she was the Director of Customer Intelligence at Telstra, and in the years since she has seen data grow as a priority, and the status of data teams within those organisations that look to take a disruptive and leadership position in the market increase in-kind.

Sandra is now the Co-Founder and Data Analytics Lead of Amperfii, an organisation that provides analytics that helps data teams measure and articulate their value back to the organisation. In this podcast, she shares insights on how she has managed teams and encouraged their deeper participation in the organisation. 

Sandra also talks about how data teams can be motivated, where they should be focusing their energies within increasingly busy organisations. She discusses how critical it is for data teams to be involved as early into the process as possible.

“You need to pick the areas where you say ‘okay, these are the big strategic things, and these are the pieces of work that I actually think really make a difference to the business,’ and focus on them,” Sandra explained in the podcast. “Even if it's only 20, or 30 percent of what you do, it's going to actually be a lot more than what you're trying to show otherwise.”

To hear more from Sandra, we are privileged to have her speaking at our Advancing AI conference in Melbourne. Click here for more information and to register to attend what will be an insightful and thought-provoking event in Melbourne, from May 3-4.

Thank you to our sponsor Talent Insights Group!

Connect with Sandra: https://www.linkedin.com/in/sandra-hogan-9409421/

Learn more about Amperfii: https://www.amperfii.com/

Join Sandra 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

2:16 Reflecting on Sandra’s experience in the industry, including lessons learned in analytics and leadership.

09:51: What are the drivers and motivators that are behind the dynamics within analytics teams at the moment?

15:32: What are the main hurdles and challenges that data teams face when aiming to maximise their impact on the organisation?

25:33: How can data teams become more involved earlier in the process, and why is this important for outcomes and team motivation?

34:15: How can organisations prove and track the value of the analytics team?

40:21: Why a “conga line” is a great analogy for the role of data teams.

43:45: Why being able to capture and articulate the value of projects is so critical to data teams.

46:02: Conclusion and final thoughts.

Mar 21, 202351:51
#200 The Constant Evolution And Future Opportunity Of Data – with Gina Papush, Former Global Chief Data & Analytics Officer at Cigna

#200 The Constant Evolution And Future Opportunity Of Data – with Gina Papush, Former Global Chief Data & Analytics Officer at Cigna

For our milestone 200th Data Futurology podcast, we have the immense fortune of being able to host Gina Papush, the former Global Chief Data & Analytics Officer of wellness and insurance company, Cigna.

Papush has a long history in data science, having been involved in modelling and coding from before the time where “data scientist” was a defined role. In the years since, she has observed that enterprises have become siloed across computer science, data science, and other roles, and that the next stage of data science evolution now is to now break those silos down and find ways to bring cohesion across the organisation.

She has also seen the role of the CDO and their remit evolve, from one that focused on governance and controls, to being a value creator within the organisation. Being an effective agent for change has been important to that evolution, she says on the podcast, and data executives need to look to the “blind spots” that they might have. Many have the technical skills to excel in analytics, but building skills in influence and thought leadership, and to be a partner to the other stakeholders of the organisation, is the next critical step for the CDO.

Finally, Papush also shares her insights on how value is extracted from data. A “one size fits all” approach cannot work, she says, and organisations need to build their strategies based on the maturity of their own data practice, rather than the hype in the market.

Once the maturity is there, she says, data scientists can start looking at real life-changing innovation. “It’s (data) a huge part of how we move healthcare to be more preventive and more interactive,” she said. “Health is currently very event driven. But analytics and AI could make it much more seamless and unlock real-time care.”

Tune in to the full podcast for more of Papush’s thoughts on the history and future of data science.

Thank you to you our sponsor, Talent Insights Group!

Join us for one of our upcoming events: https://www.datafuturology.com/events

Join our Slack Community: https://hubs.li/Q01gKNBn0



Mar 21, 202342:04
#225: The Future of Data Collaboration with Fluree

#225: The Future of Data Collaboration with Fluree

This week on the Data Futurology podcast, we have a special guest, Eliud Polanco, to talk about an innovative approach to data management and access. This approach promises to make models and the management of data more reliable and secure.

Polanco, who is the president of Fluree, looks to a blockchain-driven future for data, where data blocks sit on the ledger, and the ability to access and modify them is based on a zero trust approach.

This unlocks innovation, Polanco says on the podcast, allowing organisations and individuals to take greater control over data, and do so in a more efficient manner. He points to GDPR regulations as a good example of where this approach can help. Currently, GDPR regulations require a lot of paperwork, but it’s inefficient and often ignored by both consumers and organisations. However, through a blockchain-based, decentralised approach to data management, a person’s right to control their data can be enhanced, but in such a way that the organisation can also manage the data more efficiently and effectively.

Polanco also provides an excellent potential use case in the financial services space. Financial services have strict regulatory requirements to monitor for money laundering and other illegal activities. However, that can be difficult to do based on data privacy and other regulations. In the example Polanco gives, it is difficult for a US financial services organisation to monitor transactions with Singapore, because US organisations can’t easily get access to Singaporean financial data.

However, this decentralised approach opens up the opportunity to have automation query data and return answers without the agent ever needing to see or touch the data. Suddenly it becomes possible to note a transaction without seeing the data of the transaction itself.

Enjoy this in-depth and nuanced discussion about one of the more exciting innovations on the data horizon.

Connect with Eliud Polanco: https://www.linkedin.com/in/eliud-polanco-977529131/

Contact Fluree: https://flur.ee/contact/

Read Fluree's Whitepaper on Data-Centric Architecture: https://flur.ee/wp-content/uploads/2023/01/Data-Centric-Technology-Architecture.pdf

Thank you to our podcast 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

Feb 28, 202347:12
#224: The role of a CTO in driving a data-driven enterprise, transformative technology strategy and the human side of an agile culture. With KFC Canada’s CTO, Nastaran Bisheban

#224: The role of a CTO in driving a data-driven enterprise, transformative technology strategy and the human side of an agile culture. With KFC Canada’s CTO, Nastaran Bisheban

This week’s guest on the Data Futurology podcast is a 30-year veteran of technology, across many different segments and roles. Nastaran Bisheban, now CTO of KFC in Canada, has previously held roles at Rakuten Kobo, Canadian Tire, and RIM. She additionally sits on the Board of Directors for the CIO Association of Canada.

One of the biggest changes that have occurred over the course of Nastaran’s career is the deeper integration of technology roles into the broader business. As she explains in the interview, Nastaran completed an MBA at Harvard Business School because her role has become an even 50/50 split between managing technology and interacting strategically with the rest of the C-suite and groups within the business. Having that broader understanding of business has been enormously valuable to her career.

Beyond that, Felipe and Nastaran discuss the role that AI and machine learning is playing in KFC Canada’s operation, how executives are encouraged to spend time “on the floor” in restaurants to learn the day-to-day challenges in operation, and how the company is looking to align its business strategy and data practice to drive growth across the operation.

Ultimately, as Nastaran stressed, technology is there to support the human element of the company. Technology that is used to help identify blind spots or notice trends that can be addressed, is an example of technology that has been deployed effectively. For instance, KFC Canada recently completed a significant transformation project to allow for all the delivery and ordering apps to integrate into its systems. It was Nastaran and her team’s human-centric approach to the application of that technology that allowed the project to succeed.

Enjoy this in-depth and insightful podcast!

Thank you to our sponsor, Talent Insights Group!

Connect with Nastaran Bisheban, Chief Technology Officer at KFC Canada: https://www.linkedin.com/in/nastaranb/

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 Why you need to follow the problem (opening quote)

03:17 Nastaran shares her role and remit at KFC.

04:41 What is Nastaran’s vision for a data driven enterprise in her role as a CTO?

05:47 “Following the data” – what are the projects that KFC has undertaken with this mission in mind?

09:18 How data can be collected as an asset, but become a liability.

10:41 How AI and machine learning is being leveraged to unlock innovation at KFC Canada.

13:09 Is there a point in a technologist’s career where they make the transition to focusing more on the human side of technology?

16:55 How KFC Canada is looking to align the business strategy and data practice to drive growth through the organisation.

20:06 What makes a good CTO?

23:14 What should a technologist do to learn about the business, outside of the technology?

25:37 How organisations can prepare technical people for business roles.

28:13 How an organisation of the scale of KFC Canada looks to balance out the need for innovation with the need for stability.

31:27  Clearing technical debt is important, but how can a technology team determine what areas to focus on first?

32:49 How Nastaran and her team overcomes resistance and gets buy-in to their projects.

34:44 How KFC Canada built and managed its technical team culture.

Feb 21, 202337:19
#223: Thinking about AI and strategy in 2023 Getting the tactics right and driving value across the organisation

#223: Thinking about AI and strategy in 2023 Getting the tactics right and driving value across the organisation

In this latest episode, Felipe takes an in-depth look at the strategy and tactics behind AI and modelling, and looks at where organisations might be driving through the year.

One of the first things to understand is the difference between tactics and strategy. Strategy is the broad view – the understanding of where the organisation wants to be, while tactics form the pathway on how to get there. Too often organisations mix tactics and strategy up, and allow a narrow focus to dominate their approach to data, models and AI.

By looking at the big picture, 2023 will see an explosion in the number of models that are created, and the proliferation of AI and machine learning across the enterprise. Currently, the focus is on a small team of data scientists creating models of high value, but the future will see the number of models being created balloon out to thousands, driven by AI across the organisation.

There will also be an ongoing trend that more people across the organisation develop a basic understanding of models and AI, so they can deploy and monitor these models within their own teams. The question then becomes what does this mean to the data scientists? As we discuss, the role of data scientists will remain as critical as ever in creating those big-value, transformative models and managing the change management across the organisation. Indeed, as teams are increasingly able to create the smaller models for themselves, the role of the data science team as disruptive innovators is only going to become greater.

Tune into the podcast for these insights, and many more.

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 Welcome to Data Futurology

02:34 The one-line summary of what a data analytics and AI strategy is.

05:19 Thoughts on the growth of data models. Currently, organisations have a small number of models in production, but the future will see organisations running with thousands of models in production.

7:51 The way that I like to apply automated machine learning solutions – as a first pass of models and a first benchmark before deploying something at scale.

9:26 The two “extreme” approaches to creating AI in organisations and kickstarting the journey towards deriving value from them.


 Quotes:

  • Strategy defines where you are, as an organisation, looks at where you want to be, and then fills in the path in-between, which is where the tactics come in.
  • We’re going to go from 10, to 20, to 1000, and then 10,000 plus models in production. This is exciting – a little scary, but it’s definitely the case that we will want to have AI embedded throughout the organisation, supporting every decision in every process.
  • We can have a workforce that can create machine learning models, and help themselves and their teams on the daily tasks… we’re moving towards a world where more people in the organisation have a little knowledge of machine learning and AI.
  • We will always need that team of specialists to be working on the high value items, and to improve the models that have been created by people in the business.



Feb 14, 202314:04
#222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores

#222 Hiring And Retention In 2023: Positioning Your Organisation with the Right Audience, with Felipe Flores

Organisations face multiple challenges when it comes to building teams in 2023. On the one hand, there is a skill shortage in just about every field of data science and analytics. Finding and attracting the best people to the organisation can be difficult.

On the other hand, there is mobility between jobs unlike anything we’ve seen before. The “Great Resignation” is still a major trend sweeping across Australia, and employees will be more than willing to move on if they don’t feel like they’re getting what they need from their jobs.

In this episode with Data Futurology podcast host Felipe Flores (a Chief Data, Analytics & Technology Officer himself), he explores both sides of this particular coin. In the first half, Felipe shares key insights and tips on how to recruit the best talent, including mistakes that he’s made in hiring and how he now looks at the interview and hiring process.

The second half of the podcast is dedicated to providing tips for retention. Contrary to the popular view, it’s not always a matter of remuneration. Indeed, studies consistently show that this is far less important to many employees than things such as the opportunity to build their skills or engage more deeply with their organisation. As Felipe says “People might want to become a product owner, or a strategic person that interfaces with the business and helps them to contextualise the results to the organisation.”

Tune into the podcast for these insights, and many more.

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 Welcome to Data Futurology

0:29 What to expect from our upcoming event on operationalizing security for business value, impact and scale, at the Sofitel Wentworth in Sydney on March 14 and 15.

2:20 What makes hiring so challenging.

2:50 Three tips for hiring. Tip #1: Attitude.

3:40 Three tips for hiring. Tip #2: Transparency and openness.

5:53 Three tips for hiring. Tip #2: Be impressed with one technical thing in one technical area.

7:54 Why retention is important, and what is being done to improve it?

9:54 Three tips for retention. Tip #1: Provide formal training.

10:24 Three tips for retention. Tip #2: Give employees exposure to new work/projects.

11:31 Three tips for retention. Tip #3: Provide on-the-job training


Quotes:

  • Even having technical tests doesn't really show the full depth and capability of a person. It’s very easy to get it wrong.
  • When I was more junior in my hiring career, I would test people in the interview. We always had a technical test, and then an interview where we were going through the code, they were just wrong. This is terrible, but when I was junior, I would sometimes tell people “Hey, that’s wrong.” The idea was that if someone responded “oh, yeah, let’s discuss that” then those were the people we wanted to hire. That’s not a very effective way to do it.
  • I don’t look for somebody to impress me with general data engineering or data science skills. Rather, it could be something like the way they use one algorithm in a particular way.
  • You want one technical thing that people do well because it shows passion, commitment, and that they really care.

  

Feb 07, 202313:41
 #221 Building A Data & Change Management Strategy to Enable Smarter Data Sharing and Innovation in Queensland

#221 Building A Data & Change Management Strategy to Enable Smarter Data Sharing and Innovation in Queensland

This week on the Data Futurology podcast we are thrilled to welcome Tamara Mirkovic. Mirkovic is a specialist in data platforms, AI, machine learning and predictive analytics, and was recently the recipient of the Women In Digital National Award.

Mirkovic has experience building all-of-business data strategies and leading the change management program to get the organisation motivated behind the transformation.

It’s not easy, as Mirkovic said. When individuals and teams are already capable with data within their own silos, adopting a new platform can raise concerns for everything from cybersecurity to job stability. Even with the support of a visionary leadership team driving efforts from the top down, the success of a change management program relies on finding the right people to act as the champion within the peer group.

From there, it’s all about building a repeatable approach to the applications and models that will allow it to be rolled out to other teams across the organisation. “When you’ve got a critical mass of use cases, then everything going forward can be a version and iteration on what’s already been done,” Mirkovic said. “Our intention is to create enough patterns that can then be reused very quickly for other use cases.”

Finally, Mirkovic discussed what it means to have won the Woman In Digital award, and the challenges and opportunities that face women in the sector in general.

Tune in to hear these insights, and many (many) more from this wide-ranging and detailed interview!

Please note: The opinions expressed by Mirkovic in this podcast are hers alone, and not a representation of the organisations she has worked for and mentions.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Connect with Tamara:  https://www.linkedin.com/in/tamaramirkovic/

To learn more about Women in Digital: https://www.linkedin.com/in/tamaramirkovic/

https://womenindigital.org/

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 Welcome to Data Futurology

2:36  Introduction to the podcast and guest, Tamara Mirkovic.

3:09 An overview of Tamara’s experience and current role.

4:08 On the subject of building a data strategy program from the ground-up: what has been the most effective approach?

5:16 When looking at a wide-scale transformation project, how do you break it down?

7:25 What are some of the major challenges that you’ll come across when undergoing digital transformation and data transformation?

11:42 What can be done to encourage the data scientists within the organisation to change the mindset around to use common tools and approaches, to build a more collaborative environment?

17:55 How did the leadership team support the initial idea, and then the resistance that came at first?

20:22 How did you go about finding that group of champions that would help drive the change management and success of the project?

23:34 What are some of the common fears that come up during change management, and how do you address those concerns? 


Jan 31, 202344:26
#220: Expected Innovations In Data Science, AI & Machine Learning Over the Next 18 Months With Felipe Flores, Podcast Host & Data Futurology Founder

#220: Expected Innovations In Data Science, AI & Machine Learning Over the Next 18 Months With Felipe Flores, Podcast Host & Data Futurology Founder

The past 18 months has been a period of unprecedented innovation across data science, machine learning, and AI. The depth of research and what has been brought to market has empowered data scientists in ways that, even a year ago, could not have been predicted.

Looking forward to the next 18 months, the industry is not going to rest on its laurels, but the question is where the next waves of innovation will come from. That is what Felipe discusses on this episode of the Data Futurology podcast.

He highlights four areas in particular where he would like to see the industry focus its innovation. Starting with the ease in which to undertake data preparation, and moving through to developing better machine learning ops and engineering, the combined “key areas of innovation” would allow people working in data science and beyond, into citizen data science, to better leverage the opportunities of AI and machine learning, and at speed.

Felipe then rounds the discussion out with a look into the ethics of data science. There is a lot more discussion that needs to happen in this area, he argues, so that organisations of all sizes across the world can be sure that they’re delivering the models that have the positive impact on the world that we’re all looking for.

It’s going to be an exciting year ahead for everyone involved in data science! Tune into the podcast for more insights.

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

1:40 An overview of the innovation that has been brought to data science over the past few years.

2:33 Four areas where the industry can innovate further #1: Making it easier to do data preparation.

4:40 Four areas where the industry can innovate further #2: Democratising AI and empowering the citizen data scientist.

6:45 Four areas where the industry can innovate further #3: Automated machine learning can still be improved.

8:33 Four areas where the industry can innovate further #4: Better machine learning ops and engineering is important to being able to reliably deploy, monitor track and alert.

12:22 Do we have the data that we need to make the responsible models that we want to?


Quotes:

  • Understanding what version of data was used for a particular model, and being able to create an end-to-end link is still largely an unsolved problem.
  • We have to move into a world where more people in the organisation need to be able to have these AI tools at their fingertips, be able to use them and be able to get them to a point where there’s value being created from them. The barriers are still typically too high.
  • I’m not saying that the algorithm itself needs to improve, as that’s happening with research. Rather, this is around the creation of algorithms at speed and at a scale in a way that’s more reliable and flexible, which will make it more accessible, and increase the breadth and reach of AI in organisations.
Jan 24, 202314:59
#219 Building Successful Product Practices Around Data with Booking.com’s Director of Data Science and Machine Learning, Sanchit Juneja.

#219 Building Successful Product Practices Around Data with Booking.com’s Director of Data Science and Machine Learning, Sanchit Juneja.

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.



Dec 14, 202234:52
#218: The pressing need to build frameworks for ethical AI: Cortnie Abercrombie CEO of AI Truth

#218: The pressing need to build frameworks for ethical AI: Cortnie Abercrombie CEO of AI Truth

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




Dec 07, 202244:55
#217 AI regulation is a global concern - Where will Australia fit in among China, the US and EU? With Felipe Flores, Data Futurology Founder and Podcast Host.

#217 AI regulation is a global concern - Where will Australia fit in among China, the US and EU? With Felipe Flores, Data Futurology Founder and Podcast Host.

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.



Nov 30, 202215:36
Episode 216: Building Data Products from “In The Trenches” - With Ann Sebastian

Episode 216: Building Data Products from “In The Trenches” - With Ann Sebastian

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.



Nov 23, 202236:01
Data Futurology Podcast Episode 215: How data skills are putting digital specialists at the centre of organisations.

Data Futurology Podcast Episode 215: How data skills are putting digital specialists at the centre of organisations.

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.

Nov 16, 202244:30
#214 The Three Steps Of A Successful Data Strategy with Felipe Flores, Data Futurology Founder and Podcast Host

#214 The Three Steps Of A Successful Data Strategy with Felipe Flores, Data Futurology Founder and Podcast Host

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.




Nov 09, 202215:33
#213 Solving the challenges of our times with massive graph analytics with Dr. David A Bader, Distinguished Professor at the New Jersey Institute of Technology

#213 Solving the challenges of our times with massive graph analytics with Dr. David A Bader, Distinguished Professor at the New Jersey Institute of Technology

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.

Nov 02, 202234:17
#212 Structuring Volvo For Operational Success with Leonard Aukea, Head of Machine Learning, Engineering & Operations at Volvo

#212 Structuring Volvo For Operational Success with Leonard Aukea, Head of Machine Learning, Engineering & Operations at Volvo

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.

Oct 26, 202245:41
#211 Accelerating MLOps with Amazon SageMaker with Romina Sharifpour, Machine Learning Specialist at Amazon Web Services (AWS)
Oct 20, 202257:57
#210 The Government Transformation That Created Australia’s Top Analytics Leader, with Brad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs

#210 The Government Transformation That Created Australia’s Top Analytics Leader, with Brad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs

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.

Oct 12, 202237:41
#209 How Successful Transformation Is Driven By Data Engineering Excellence With Richard Glew, Chief Technology Officer, and Natalia Dronova, Senior Data Analyst from Aginic

#209 How Successful Transformation Is Driven By Data Engineering Excellence With Richard Glew, Chief Technology Officer, and Natalia Dronova, Senior Data Analyst from Aginic

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.

Oct 05, 202243:29
#208 How Ethical AI is more than an obligation; It is an opportunity with Natalie Rouse, General Manager of Eliiza and Brendan Nicholls, Practice Lead, Machine Learning Engineering

#208 How Ethical AI is more than an obligation; It is an opportunity with Natalie Rouse, General Manager of Eliiza and Brendan Nicholls, Practice Lead, Machine Learning Engineering

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.

Sep 28, 202241:59
#207 From health to existential crisis: How data can be the solution With Yalchin Oytam, Head of Clinical Insights and Analytics at South Eastern Sydney Local Health District (SESLHD)

#207 From health to existential crisis: How data can be the solution With Yalchin Oytam, Head of Clinical Insights and Analytics at South Eastern Sydney Local Health District (SESLHD)

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.

Sep 21, 202241:22
#206 The New Horizons For Data And Healthcare Are Exciting For Patients, with Precision Driven Health’s CEO, Kevin Ross

#206 The New Horizons For Data And Healthcare Are Exciting For Patients, with Precision Driven Health’s CEO, Kevin Ross

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.

Sep 15, 202237:20
#205 On Leveraging AI To Grapple with Humanity’s Biggest Questions with NVIDIA’S VP of Solutions Architecture and Engineering, Marc Hamilton

#205 On Leveraging AI To Grapple with Humanity’s Biggest Questions with NVIDIA’S VP of Solutions Architecture and Engineering, Marc Hamilton

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.

Sep 13, 202240:27
#204 Data Is The Foundation That Makes Digital Transformation Sing With Harjot Singh, former CDO of RAC

#204 Data Is The Foundation That Makes Digital Transformation Sing With Harjot Singh, former CDO of RAC

This week we are thrilled to welcome Harjot Singh, who had been the CDO of RAC in WA until very recently. Singh is a true expert of and champion for transformation in the workplace, and has deep insights on data strategy and data governance to share.

“Data drives a digital transformation in any organisation.” Singh said. “Everyone wants to jump on the bandwagon around digital transformation, but most organisations struggle to understand where to start.

As Singh explains on the podcast, there is a specific order with which organisations need to approach transformation. It starts with data, which organisations should already be investing in because monetising data is one of the biggest opportunities in business.

From there the data can be leveraged into machine learning and, eventually, AI.

Related to this, however, Singh also mentions that organisations need to better understand the business drivers behind what they are doing with data and the digital transformation journey. “I wrote an article on LinkedIn that was around the five common mistakes to make in digital transformation,” Singh said. “If people think that digital transformation is only about technology transformation, it’s going to fail.”

From there, Singh and host, Felipe Flores, discuss the impact of regulation in Australia on innovation, how companies are working within those challenges, and how various highly regulated sectors – including insurance and financial services – are finding new opportunities.

Ultimately, however, as Singh says, it all comes back to data. “I say data and digital in the same sense, because I treat them as two sides of the same coin – one is incomplete without the other. Data is the bullet and digital is the gun to launch the bullet – without both you’re not going to have much of an effect.”

For deep insights on the strategy and opportunity behind digital transformation, and the deep role of data in it, check out the full podcast!

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!

Read the full podcast episode summary here.

Sep 07, 202229:57
#203 Diversity Is The $60 Billion Opportunity Australian Businesses Can’t Ignore With Azadeh Khojandi, Co-Founder & Director and Katrin Schmidt, Co-Founder & Managing Director, GEEQ Sydney

#203 Diversity Is The $60 Billion Opportunity Australian Businesses Can’t Ignore With Azadeh Khojandi, Co-Founder & Director and Katrin Schmidt, Co-Founder & Managing Director, GEEQ Sydney

GEEQ (Geeks With EQ) is a non-for-profit with an important mission: helping women get into IT and boosting the diversity of IT companies in Australia. To this day less than 20 per cent of Australia’s IT workforce are women, and this has far-reading implications, from the bias that gets built into technology itself through to the depth of innovative thinking available in the space.

The two founders of GEEQ, Azadeh Khojandi and Katrin Schmidt, join us on this special podcast to discuss the work that they’re doing, and the traction that diversity is getting across Australian corporate spheres.

“It’s important for us that we’re not only bringing women into the workforce, but helping them to grow and get the promotions, more responsibilities, and the fulfilment they deserve,” Khojandi says in the podcast. GEEQ is more than an advocacy group. It focuses heavily on skills and mentoring, providing women with books on leadership and managing events to assist with knowledge transfer.

On the side of advocacy, the two are focused on helping the Australian business community recognise biases in the hiring process and how to mitigate against that, Katrin says on the podcast. “It’s really difficult to have awareness of your own unconscious bias,” she says. “It’s like stopping and thinking to yourself, ‘what did I just do?’. The first step is a change in awareness. You don’t have to jump to conclusions, but you do need to watch and be aware.”

Azadeh and Katrin are sponsors at the upcoming Data Engineering Summit and will be hosting a luncheon. It will be a rare opportunity to talk directly to some of the speakers from the summit and discuss how to tackle the ongoing challenge of diversity.

In the meantime, tune into this in-depth podcast, and hear from the experts about why diversity is a $60 billion opportunity for Australian businesses.

Learn more about GEEQ: https://www.linkedin.com/company/geeq-australia/

Thank you to our sponsor, Talent Insights Group!

Read the full podcast episode here.

Aug 23, 202238:56
#202 Building A Unified and Uniform Approach To Data And Data Teams With Nathan Steiner, Director of Field Engineering, ANZ, at Databricks

#202 Building A Unified and Uniform Approach To Data And Data Teams With Nathan Steiner, Director of Field Engineering, ANZ, at Databricks

Later this month, Nathan Steiner, the Director of Field Engineering, ANZ, at Databricks, will give a presentation at the Data Engineering Summit. There he will talk about the “habits” of data-driven organisations, and the importance of an open architecture that combines the best elements of data lakes and data warehouses.

Steiner kindly appeared on this episode of the Data Futurology podcast to talk about this, and further discuss the Databricks vision for data-driven workspaces.

“Historically, you look at data engineers, data analysts, AI, machine learning and data scientists, they were focused on different types of data, so you had your data engineers focused on your siloed and disparate ADW enterprise data warehousing, relational database structured systems, and you had your data scientists looking at predominantly real time data,” he says during the wide-ranging conversation.

The solution, to Steiner’s and Databricks’ vision, is bringing those data resources together and making for a more collaborative data environment. “It’s more pragmatic and effective for these job roles to be working from a single uniform platform,” he says.

As Steiner notes during the conversation, the personalisation that is so important to modern business is driven from being able to make the data resources collaborative. He highlights the example of a financial services company that wants to be able to issue credit within five minutes from an application via a smartphone. “In the back end, it's AI, and ML that is doing the credit risk assessment frameworks of that particular individual and creating that value customer experience,” he says.

Finally, Steiner considers the governance implications of the Databricks lakehouse, and the advantages of having a uniform and unified approach when it comes to governance.

For more insights on breaking down data silos and unifying data teams, be sure to tune in to the podcast!

Enjoy the show!

Learn more about Databricks

Learn more about Nathan Steiner

Thank you to you our sponsor, Talent Insights Group!

Read the full podcast episode summary here.

Aug 18, 202245:43
#201 Graph Databases, Deep Analytics, And Change Management: The New Data Frontiers With Peter Kokinakos, COO of MIP

#201 Graph Databases, Deep Analytics, And Change Management: The New Data Frontiers With Peter Kokinakos, COO of MIP

Graph databases are powerful tools in analytics, but they are an often-misunderstood innovation. As they hold the relationships between data as a priority, they are an invaluable tool for modern, heavily inter-connected datasets.

In this episode of Data Futurology, we explore graph databases with Peter Kokinakos (pk), the COO of MIP. They have been conceptualised for around 18 years, but it is only now that the computing power has started to catch up to allow graph database projects to come to fruition.

MIP is right at the front of delivering these capabilities to their customers. “It’s becoming a real product,” Kokinakos says in the podcast. “All of a sudden we’ve got the capability of delivering these really intricate kinds of analytics for complex relationships.”

Kokinakos, who will be speaking at the Advancing AI Sydney summit in August, further outlines the additional value that data scientists can get out of data relationship value in comparison to the data value. Delivering this value requires some change management to take advantage of because, as he says, “instead of just double clicking on something and drilling down the level, you can now actually drill down by the relationship.” However, once that change management process has been completed, the ability to be able to interact with customers on the basis of interconnected relationships rather than single data points is compelling.

Change management is a challenge for many organisations and data scientists – anything new is always going to have some resistance. This is why MIPS runs The Data School, and Kokinakos explains in detail the value that adds to customers in the podcast as well.

Tune in for an in-depth discussion into the very bleeding edge of data innovation with a company at the forefront of it.

Enjoy the show!

General info about the Data School

Application process and deadline for the next 3 intakes: https://www.thedataschool.com.au/apply/

Learn more about MIP

Thank you to you our sponsor, Talent Insights Group!

Join us for one of our upcoming events: https://www.datafuturology.com/events

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

Read the full podcast episode summary here.

Aug 10, 202241:28
#200 The Constant Evolution And Future Opportunity Of Data – with Gina Papush, former Global Chief Data & Analytics Officer at Cigna

#200 The Constant Evolution And Future Opportunity Of Data – with Gina Papush, former Global Chief Data & Analytics Officer at Cigna

For our milestone 200th Data Futurology podcast, we have the immense fortune of being able to host Gina Papush, the Global Chief Data & Analytics Officer of wellness and insurance company, Cigna.

Papush has a long history in data science, having been involved in modelling and coding from before the time where “data scientist” was a defined role. In the years since, she has observed that enterprises have become siloed across computer science, data science, and other roles, and that the next stage of data science evolution now is to now break those silos down and find ways to bring cohesion across the organisation.

She has also seen the role of the CDO and their remit evolve, from one that focused on governance and controls, to being a value creator within the organisation. Being an effective agent for change has been important to that evolution, she says on the podcast, and data executives need to look to the “blind spots” that they might have. Many have the technical skills to excel in analytics, but building skills in influence and thought leadership, and being a partner to the other stakeholders of the organisation, is the next critical step for the CDO.

Finally, Papush also shares her insights on how value is extracted from data. A “one size fits all” approach cannot work, she says, and organisations need to build their strategies based on the maturity of their own data practice, rather than the hype in the market.

Once the maturity is there, she says, data scientists can start looking at real life-changing innovations. “It’s (data) a huge part of how we move healthcare to be more preventive and more interactive,” she said. “Health is currently very event-driven. But analytics and AI could make it much more seamless and unlock real-time care.”

Tune in to the full podcast for more of Papush’s thoughts on the history and future of data science.

Thank you to you our sponsor, Talent Insights Group!

Join us for one of our upcoming events: https://www.datafuturology.com/events

Join our Slack Community: https://hubs.li/Q01gKNBn0

Read the full podcast episode summary here.

Aug 03, 202242:04