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

On this episode of the podcast, we have the privilege to speak to Michelle-Joy Low, Ph.D, the Head of Data & AI at Reece Group. Low talks us through how data is the foundation of the ongoing transformation of one of Australia’s most venerable retail brands, having celebrated 100 years in 2020.

Low explains the importance of diversity in the workforce, and how it leads to better outcomes for the business and better outcomes for the customer. The tech space has been particularly good at recognising the importance of diversity, Low said, and Australia is a great place to work in that regard, but at the same time it’s now important to look beyond participating in the movements, and genuinely build diverse teams that are empowered to speak out about and drive further change.

Low also shares some of the challenges that come from AI, and how she and her team are grappling with them. For example, data is complex to implement and expensive… and the decision makers behind the data projects are distant from the team that builds the applications. The Chief Customer Officer, for example, doesn’t build apps themselves. So how do you tackle that challenge within the data team and deliver outcomes for the organisation?

Tune in for this deep-dive and fascinating conversation about how a business is leveraging data to drive toward better outcomes for all.

Enjoy the show!

Thanks to our sponsor, Talent Insights Group!

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Read the full podcast episode summary here.

In this second podcast with the visionary data scientist, entrepreneur and business leader, Sarah Dods, we dive into the details around product development and the role of data science.

Across her career, from NICTA and CSIRO, through to AGL, Telstra Health, RMIT University and Gerson Lehrman, Sarah has had a long history in bringing innovation to market.

One thing she says is that business leaders should not lose sight of how data science needs to work. It’s a team sport, in the same way that building a car requires more than someone to design and build the engine.

“So, what do you need to make a solution supportable, sustainable and safe?” Sarah asks, before going on to note that another challenge that data science teams need to be cognizant of is that data science models fail silently. The application could be bringing garbage in, and pushing garbage out, and it will happily keep working, producing outputs that will no longer mean what you want them to. So teams need to think about your feedback loop.

This is where agile comes from, in encouraging an iterative process in which the MVP is produced as quickly as possible, and then iterated on indefinitely as opportunities for ongoing development arise.

Stay tuned for some deep insights from Sarah about the opportunities for data science to drive next-level product innovation.

Enjoy the episode!

Read the full podcast episode summary here.

Thanks to our sponsor, Talent Insights Group

Discover what the Data Futurology Community is up to...Join here

On this episode of the podcast, we are excited to speak to Sarah Dods, one of Australia’s top tech entrepreneurs, innovators, and company leaders.

In a career that has spanned from public organisations like NICTA and CSIRO, to private companies as wide ranging as AGL, Telstra Health, RMIT University and Gerson Lehrman, Sarah has been at the forefront of bringing technology and ideas to market.

As Sarah says, one of the big challenges with data science is articulating its value. Data science costs money to develop, and data science costs money to run. So, why would somebody pay money for what you’re doing? In this episode, she shares some of her proven strategies for justifying to those outside of the data science team how the investments will create and add value.

The other great challenge that the data science team needs to grapple with, Sarah adds, is change management – how do you explain an application or product to people that have not used before?

Stay tuned as Sarah talks about how the data science teams can turn these challenges into opportunities.

Thanks to our sponsor, Talent Insights Group!

We're always striving to further the conversation around the latest AI advancements and we would love to keep you involved in future conversations. Please join our Slack community to keep in touch with the wider data community.

Read the full podcast episode summary here.

The end goal is to provide business users with trusted data, faster at lower cost. How do you build to achieve that? What approach should you take, which tools and what resources and skill-mix are required to support that?

Our Data Engineering Summit coming up on 31st August will look at exactly those demands. As we explore these challenges, we revisit a special recording from our Future Proofing Data Platforms event where our brilliant guests took a broader view and debated the pros and cons of centralised vs decentralised architecture.

In an interesting session Naveen Siddareddy, Data Solutions Architect at PlayStation advocated for decentralised platforms. Taking the opposing view was Rohan Dhupelia, Data Platform Senior Manager with Atlassian who championed for centralised platforms. Take a listen and see what you think of their reasoning and share your thoughts on which ecosystems are best suited to centralised vs decentralised structures.

Rohan will be joining us again at the Data Engineering Summit in person to offer up a retrospective on ensuring productive, adaptive, and resilient environments that are verifiable, less resource heavy and that critically produce greater outcomes. It’s going to be a fantastic event sharing lessons learned on building trust in the data that you provide and your role in enabling the business to succeed through data.

Thanks to our sponsor, Talent Insights Group!

We're always striving to further the conversation around the latest AI advancements and we would love to keep you involved in future conversations. Please join our Slack community to keep in touch with the wider data community.

Read the full podcast episode summary here.

We’re thrilled to have Dr Aleksandar Lazarevic, Former VP of Advanced Analytics & Data Engineering at Stanley Black & Decker, joining us from the U.S.! He holds a wealth of experience and shares many of his hard-earned lessons in today’s episode.

Data is Alex’s passion and his journey in the data science field started 20 years ago with his PhD in Machine Learning. Throughout his career, he’s been able to evolve from someone with a solid technical background into a full-on data executive who’s able to drive real value across the company.

Alex’s proficiency with data, AI and ML, expands across industries. He has worked in healthcare, property and casualty insurance, banking and manufacturing. No matter the industry, building a data-driven culture within the organisation is always one of the things he strives for. His diverse background has granted him a unique perspective when it comes to approaching business problems using analytics.

Stay tuned as he shares his tips on identifying the areas within your organisation where applying data science can contribute huge value, and much more!

Thanks to our wonderful sponsor, Talent Insights Group!

We're always striving to further the conversation around the latest AI advancements and we would love to keep you involved in future conversations. Please join our Slack community to keep in touch with the wider data community.

Read the full podcast episode summary here.

We are joined by Andy Sutton, General Manager Data Driven Transformation from Endeavour Drinks Group. Endeavour Group is the master brand that holds several well-loved Australian brands like Dan Murphy’s and BWS.

One of the benefits of the retail industry is the massive amount of data that is generated, not just from customers but from the operations side as well.

Andy stepped down from his previous role in Endeavour Group to dive straight into devising the organisation's data strategy and now serves as the bridge between the business and the analytics team. One of the key aspects of his current role is to identify priority use cases to work and focus on. When they started, 6 months ago, they had 275 use cases.

A vital component of their effort’s success was going back to the ‘why’. “Why do we need a data transformation team to exist?  How is it going to operate? What is it going to do?” All these questions were the starting point of their journey and helped them identify the path they would follow. Instead of focusing on just building fancy models, they describe their mission as delivering real value through data.

A lot of their initial work was prioritising and identifying the biggest opportunity areas that would bring value to the customers and organisation. Out of their initial list of 275 use cases, they narrowed it down to four, three of which are being worked on currently, and the other one will be tackled in 2023.

Read the full episode summary here: www.datafuturology.com

Thanks to our wonderful sponsor, Talent Insights Group!

We're always striving to further the conversation around the latest AI advancements and we would love to keep you involved in future conversations. Please join our Slack community to keep in touch with the wider data community.

Woodside Energy has made great strides in leveraging AI for operational success and cost savings but getting to the point where the business was ready to invest was no easy feat. Lauchlan shares how to first gain investment in all frameworks so that you can successfully layer a decision making framework and roll out an AI strategy.

Thanks to our sponsor Talent Insights Group!

Join us in Melbourne on 6th & 7th April 2022 for Advancing AI, Overcoming the barriers to adoption: https://www.datafuturology.com/advancing-ai

Read the full podcast summary here.

In this episode, Felipe sat down with Aruna Kolluru, Chief Technologist for AI at Dell Technologies ahead of our Advancing AI Melbourne event on 6-7 April. She shared how they work on providing solutions for their customers and use all available technologies - AI, IoT, data, etc, - to reach their desired outcomes.

Aruna works with clients across a vast range of industries and this is a testament to the power data & AI hold to promote growth and generate business value, regardless of the area you work in. In her own words, data has become the core of innovation for every industry.

With the key role data plays in today’s organisations, making it accessible for the analytics team to extract insights from it is of the utmost importance. That’s why data platforms have become essential for providing reliable, quality data that can be leveraged to achieve business goals.

Tune in for the full conversation with Aruna on how to leverage data platforms. You can also join her in person when she presents with David Siroky, Asia Pacific Head of AI, Data Analytics & HPC, Director, Dell Technologies at Advancing AI Melbourne (6-7 April, 2022).

Huge thanks to our Diamond Sponsors, Dell Technologies and Microsoft for supporting this event for the data community - to learn more from them register here: https://www.datafuturology.com/advancing-ai

Ant and Felipe sat down ahead of Advancing AI Melbourne (6-7 April) 2022 to discuss what executives need to be thinking about when it comes to advancing AI initiatives across the enterprise. In this candid chat they explore how artificial intelligence impacts humans and common fears around the machine. They also share on the opportunities with AI, where the machine can enhance human performance and free up time for more strategic work.

To hear more from Ant in person, register for Advancing AI where he will be on a panel covering: “Business readiness and engagement – navigating the path to business value.” Use code COMMUNITY for a special discount. https://www.datafuturology.com/advancing-ai-registration#aai-registration

Thanks to our sponsor, Talent Insights Group!

Read the full podcast summary here.

In this episode, Felipe sat down with Aruna Kolluru, Chief Technologist for AI at Dell Technologies ahead of our Advancing AI Melbourne event on 6-7 April. She shared how they work on providing solutions for their customers and use all available technologies - AI, IoT, data, etc, - to reach their desired outcomes.

Aruna works with clients across a vast range of industries and this is a testament to the power data & AI hold to promote growth and generate business value, regardless of the area you work in. In her own words, data has become the core of innovation for every industry.

With the key role data plays in today’s organisations, making it accessible for the analytics team to extract insights from it is of the utmost importance. That’s why data platforms have become essential for providing reliable, quality data that can be leveraged to achieve business goals.

Some common challenges Aruna has identified are most data and analytics projects are viewed as technical initiatives without any alignment to the business objectives, and the difficulty business users have in finding and using data that exists in a variety of different formats and locations without having a catalogue or a way to really see all the data they have. She fears some business leaders may view data platforms as just another technology improvement project, when in fact they can be the foundation that ensures data and analytics are set up to support their business goals and produce meaningful insights from all the available data. Once organisations have set up a data strategy that deals with the lineage of the data, its quality and the whole governance around it, it’s easier for the data scientists to analyse it.

Other important factors that can help level up your analytics efforts and the impact they have, according to Aruna, are investing in the right analytical tools and fostering a culture of constant learning within your company that encourages employees to improve their skill sets and keep up with the industry’s rapid pace of advancement.

Aruna also shared some of the basic steps to follow when it comes to end-to-end delivery:

A tip she offers is using examples from your peers of what they're doing or taking inspiration from other industries. You can also pick your most pressing challenges and assess how AI can be leveraged to solve them, but doing AI just because it's ‘the coolest technology’ is never the way to go.

    2. Recognise gaps in capabilities and prioritise what you want to achieve.

Think about the feasibility of the solutions and assess if you have the necessary data and capabilities.

    3. Do a proof of concept.

This will bring out any challenges that may arise in real projects and help you prepare to avoid/ overcome them.

    4. Strive to work within a scalable and adaptable infrastructure.

Technology will change, the way we analyse data and algorithms will change, so think of how you can avoid problems like data migration in the future, by choosing a platform that can scale and adapt with your needs.

Tune in for the full conversation with Aruna on how to leverage data platforms.

Thanks to our sponsor Talent Insights Group!

Read the full podcast summary here.