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

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!

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Read the full podcast episode summary here.

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

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Read the full podcast episode summary here.

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.

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For the most innovative and forward-thinking organisations, the next frontier forward for data is focused on machine learning, and specifically the role that MLOps plays in driving outcomes.

Questions that data leaders need to be asking themselves now include: What steps do organisations need to take to deliver ML maturity, how can they take the leap from experimentation to production, and how ML teams be effectively organised and motivated around this goal?

To dive deeply into this critical discussion, Data Futurology recently brought together a panel of some of the leaders in ML strategy and execution. Each of these companies have been successful in productionalising ML across their enterprises, and discuss their strategies and successes in an open and free-flowing discussion:

These four experts also highlight just how important it is to motivate teams around an ongoing process of learning and discuss how to deliver a dynamic understanding of the changing role of data across the organisation. Whether the data team is inwardly-looking, or focused on customer outcomes, emerging concepts such as “software 2.0” – as mentioned in the webinar – will continue to throw curveballs that MLOps teams will need to have the agility to adapt to and capitalise on.

Ahead of the Scaling AI with MLOps event to be held in Melbourne on October 25, this webinar is a unique opportunity to gain insight from those at the very bleeding edge of data innovation.

Enjoy the show!

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Online wagering is one of the most sophisticated and complex fields for data and analytics. This week on the Data Futurology podcast, Mia O’Dell, the GM of Data Science at Sportsbet, kicks thing off by explaining how the company brings together three separate data teams, across three lines of business, to achieve meaningful and collaborative data outcomes.

Sportsbet is also growing its data practice and looking to nearly double its team sizes by the end of the year. O’Dell – who was also responsible for scaling the data practice in a previous organisation – also shares some insights about how to approach data scaling. There’s no “one size fits all” approach, she says. Success depends on being able to work with the teams to come up with a strong and compelling vision.

Finally, O’Dell also shares her concept of “machine learning offense” and “machine learning defence” as a way to help articulate the value of ML Ops at a time where non-data executives within enterprises are still struggling to understand the breakdown and operation of ML Ops teams.

It’s also important to understand where and when ML Ops becomes important to a business, O’Dell adds, saying that a lot of organisations make the mistake of going all-out when they’re just at the start of the journey, where the value of ML Ops will be marginal and difficult to articulate.

“If your first machine learning model is something that’s extremely critical to the success of the business, of course you want to over invest in its reliance,” she says. “But for something that isn’t necessarily core to the business, ML Ops can result in putting far too much effort on the defensive side, and not enough yet on the offensive side.”

Tune in for in-depth insights into this, and more, with Mia O’Dell.

Enjoy the show!

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

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Read the full podcast episode summary here.

On today’s podcast we have special guest, Melanie Johnston-Hollitt, the Director of the Curtin Institution For Computation, to discuss with us some of the bleeding edge ways that data is being leveraged in the academic space.

For example, a new radio telescope being built in Australia and South Africa will give us new insights into the cosmos. It will also generate 160 terabytes of data per second; an eye-watering amount of data that poses unique challenges about how it’s utilised and managed. As Johnston-Hollitt mentions, where most wisdom says to store all the data collected, in this case, the research teams are better off developing ways to process the data as quickly as possible, and then removing it to make a fresh set of observations.

This understanding of how to best manage and interpret data highlights the ongoing role that data specialists play at a time where automation is taking ever-more amounts of mundane work off the hands of people, Johnston-Hollitt adds. Data automation will achieve three things in workplaces, she says:

1) It will take the drudgery away from roles, allowing professionals to focus on higher-level and more engaging & rewarding work.

2) It will supplement and complement, but not erase, the expertise of humans. Johnston-Hollitt points to how data can be used to support medical diagnosis for less common conditions that a doctor might not see frequently, but ultimately, it’s up to the doctor to make the diagnosis.

3) Data and AI will also result in the creation of new jobs, as people develop more sophisticated algorithms and need people to validate the applications and results.

Hear more detail about all these insights, and more, by tuning in now. Thank you very much to Johnston-Hollitt for guesting on this podcast.

Thank you to you our sponsor, Talent Insights Group!

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“Data engineers are like plumbers,” Edward Chenard, the Senior Director of Data Science and Analytics at Shipwell, said in this latest episode of the Data Futurology Podcast.

By that, Chenard means that data scientists are excellent at analysing what’s coming through the proverbial pipes, but if the “pipes” break for even a couple of hours the costs can be in the millions of dollars, and so you want specialised data engineers operating in the background, maintaining the stack and being the unsung heroes of the data practice.

This is just one insight on building effective data teams that Chenard shares on the podcast. In this in-depth discussion, he also:

Finally, Chenard shares his three key personality qualities that allow someone to succeed in data-based roles:

1) Creativity. If you don’t look at the space as a creative endeavour, you’re not going to be happy.

2) Curiosity. If you’re not naturally going out there and looking up stuff, going down different rabbit holes you’re not in the right mindset.

3) Openness. You need to be open to other perspectives – including those you don’t agree with – to be able to see the opportunities that come from data.

Tune in for these insights, and many more, around the dynamic roles and exciting opportunities facing data scientists ahead.

Thank you to you our sponsor, Talent Insights Group!

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

Read the full podcast episode summary here.

From skills shortages to remote working, keeping data professionals happy and comfortable in their roles is a rapidly evolving challenge. This week on the podcast, Talent Insights Group Director, Ben Le Gassick, and Associate Director, Patrick Choy, are our special guests as we delve into the problems organisations face in attracting and retaining the best people.

The talent shortage means that many employees feel overworked and overwhelmed. Meanwhile, while remote work has become standard and expected, making sure that people continue to feel engaged and connected within their teams and organisation is important to avoid attrition within teams.

On the podcast, Patrick and Ben discuss solutions to these pressing problems. It could be as simple as understanding the strengths and interests of each individual data scientist, and ensuring that the work meets their personal and professional goals. Another best practice is to leverage contractors intelligently to supplement the permanent staff. It’s also important to consider how to keep the data scientist engaged once the application that they were working on has been deployed and the workload shifts to maintenance. Many data scientists leave a role after a year simply because maintenance work isn’t as engaging, so what is the solution there?

And, finally, Patrick and Ben also share their thoughts about what employees like to see in a leader. People are attracted to great leaders and even willing to follow them from one company to the next. So, what can an organisation do to make sure that they are the ones with the great leadership team?

Tune in for these insights, and many more, about the opportunities and challenges in recruitment for data roles in 2022 and beyond.

We will be covering this topic in more detail at Advancing AI Sydney with our awesome community. To join us there in person register with the earlybird discount at: https://www.datafuturology.com/advancing-ai-sydney

Read the full podcast episode summary here.

This special episode is a recording from a live webinar we ran back in February as part of our Future proofing your data platforms online event covering how to establish a best in class multi-cloud strategy.

Felipe explored all aspects of a multi-cloud strategy, including simplifying your data architecture, regardless of whether your systems are running on-prem, in the cloud, or a combination of both and optimising your agility and efficiency across your cloud infrastructure.

Stijn explains that there are three main drivers that organisations should consider in a multi-cloud strategy: cost, optimisation and capability. Each cloud provider to a certain extent has the same capability, but they have their niches in certain products that they offer.

Kieran tells us that when you're on a multi-cloud journey, you need to go into it with your eyes open. People underestimate the process of how much it can cost and how long it can take in a big organisation.

So, what does it take to jump into a world of multi-cloud and is it right for your organisation? Tune in to this podcast to hear from Kieran and Stan on the best approach for a Multi-Cloud Strategy.

Enjoy the show!

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Australia’s CSIRO has always been a hotbed of innovation and technology creativity, which is why it is a special privilege to speak to Stela Solar, the Director of the CSIRO’s National Artificial Intelligence Centre on this week’s podcast.

Solar, who spent over a decade working in global roles at Microsoft in the US prior to joining CSIRO in January, said that one of her key observations on coming to Australia is that we are enthusiastic adopters of technology – we’re a leading nation on cloud adoption – we currently run a little behind on AI adoption.

There are three areas where Australia can invest to start to narrow the game and realise the opportunity of AI adoption, Solar said: Addressing the skills shortage, broadening the investment scene beyond the US, and the development of “tech cities” where the population of the area is galvanised around technology.

Another big opportunity for AI in Australia, Solar added, was for the small and medium-sized business. Just 2% of Australian businesses are currently leveraging AI solutions. This is a low base, but it also means an enormous opportunity for businesses looking forward. In the podcast, Solar shares what that might look like.

Tune in for these insights and more from one of the foremost data thought leaders working in Australia.

Enjoy the show!

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Read the full podcast episode summary here.