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

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.” --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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 --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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). --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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? --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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.” --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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.” --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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 --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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 --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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.    --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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?  --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

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. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message