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

In part 2 of our interview with Abhi Seth, he tells us that part of his role is to really drive scale for analytics across the ten businesses within TE. We learn how the adoption of analytics is enabled throughout the organization and a big component of that is driving, understanding and building capability within the Centre of Excellence (COE). He says the first 90 days in the COE is about building capability and being able to have experts in data science, cloud, dev ops, ML Ops, data visualization, user experience, storytelling, and data engineering. Abhi says his focus is now on building a small COE team within each of the businesses and moving to a  “hub & spoke” model as the analytical maturity of the organisation improves. Abhi goes on to tell us about how he creates and enables “seed teams'' and how it’s important to ensure the problems you're solving are creating value for the company and are tied to a strategy. He also says you should have a committed executive sponsor. Throughout the episode we discuss the results of our poll questions: Does your organization have a data science or analytics Center of Excellence? How is your organization's cloud migration going? Does your organization centrally manage the delivery of analytics across the enterprise? Does your organization measure the success of the analytics function? Does your organization develop their analytical talent? Enjoy the show! Read the full episode summary here: Ep 163 Thank you to our sponsor, Talent Insights Group. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Abhi Seth started his journey as an industrial engineer which he describes as his passion, and then found his way into research. His PhD is in mechanical engineering, and human-computer interactions, which he says was kind of a double major PhD. Abhi started working with virtual reality applications for mechanical engineering and says back then it was about how to simulate environments and models where you create a suspension of disbelief for a user. So he set out to prove that VR was also valuable in solving real industrial problems which led him to join the Global R&D centre for Caterpillar, the world’s largest mining construction equipment manufacturer. Abhi currently leads a global data analytics team for TE Connectivity, a $14B electronics manufacturing company. He says he is always up for a challenge and learning something new, one of the reasons he decided to join TE Connectivity. We learn from Abhi about the purpose of centres of excellence, the differences between data science COE, and a centralized department and how do you measure success with analytics. Enjoy the show! Read the full episode summary here: Ep 162 Thank you to our sponsor, Talent Insights Group. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In part two of the Ultimate Uplevel for Business-focused Data Professionals, with Lillian Pierson, we learn about her data action strategy plan. She created a data evaluation use case workbox with 31 use cases broken down by industry and function and tells us that if you are innovative then it's actually everything you could possibly need. You don't need to read 100 use cases. Lillian says you should survey industry use cases to find what's possible. Take stock of your company, look at where the biggest gap for a data solution is, and then assess possible options against use cases. Aim for projects that will make an impact within 3 months. Enjoy the show! Read the full episode summary here: Ep 161 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

We are joined by Lillian Pierson, a very well-known figure in the data space. She started her own business as a data consultant around 2013 and eventually started helping others become better data leaders and entrepreneurs. Currently, she has helped train and educate over a million data professionals! Lillian is not only a great data business mentor, she is also the CEO of Data Mania and a data product manager. She touches on the exponential growth the data science industry has had in the past few years and shares how hard it was for her to get useful data content online when she first started. That’s part of why she is interested in helping others and making knowledge available to everyone. Quotes: "I've been able to reach and help train and educate over a million data professionals. I turned the business on its face a bit, I have a community of 650,000 data professionals. So instead of working as a data consultant, I started helping other data professionals become better data leaders and entrepreneurs. And so that is, that has been the core focus of our business for two years, and I'm committed to that not changing in this business ever." "You know people can learn how to be successful, they can learn how to create a successful product, how to run a successful business, how to make a difference in their career. So you show them the processes and the steps that they need to take in order to do that." Enjoy the episode! Read the full episode summary here: Ep 160 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In part 2 of this episode, Nitish shares his thoughts on how to ensure that the data scientists expertise are well utilized. He says their key metric last year for the entire team was to hit exactly one number - 100 active users for their data platform with only 50 data scientists! He also says organizations should focus on enabling self service across the business. Nitish talks about the data support channel at Afterpay which allows for anyone to ask questions about the data and the data platform. He says find the people naturally drawn to the data and support them with assistance, training and coaching. Enjoy the episode! Read the full episode summary here: Ep 159 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that this is for the next 10 years, but building with the view that they will need to get rid of this in two to three years. Nitish says when a data scientist joins a company they should be able to query a data lake or data warehouse like any library. We need to help users find information quickly so they can actually get the information that they want to do their research immediately. It needs to be organised so people know how to find what they need. We end part one with a discussion on how organisations should use coaching to get the best results. Ultimately, leadership's role is to get the right outcomes and support people with coaching, technology, decision making and resources.  He believes the goal is not to build the fastest product. The goal is to make sure your data scientist colleagues are happy. And for them to be happy, you need to make sure that she or he is able to do their job fast, which involves giving proper easy to use performance tooling, giving good data, and then making sure that on a daily basis, it works. Enjoy the episode! Read the full episode summary here: Ep 158 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In Part two of this episode, Nonna talks about the role culture plays in a data-driven organization and the importance of role accountability and the support of leadership. One of the things that Nonna strongly subscribes to is the RMIT vision to support their students in every possible way including a new initiative called the Data Innovation Hub which gives students 12 months of real industry experience. Nonna says their key focus at RMIT  is to make sure that their students are ready for life and work. Also in this episode, we discuss why a widely known and explicit data strategy is vital to every organisation. Nonna says that everything is about data, and it doesn't matter what your organization is or does, because, without data, it's not going to be successful. In this episode we discuss: How using interns can benefit your organization Data Owners vs Data Trustees Working with peers and data leaders across an organization The use of data analytics during Covid-19 Do executives trust the metrics Enjoy the show! Read the full episode summary here: Ep 157 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Born in Moscow Russia, Nonna got her first degree in Engineering but after emigrating to Australia, soon realised there were few engineering jobs around.  Nonna says she had no choice but to do more study and decided on a master’s degree in project management from RMIT. Nonna has spent the last two years at RMIT building a data and governance strategy. In this session, she shares her journey, as well as how they set about establishing a culture for data governance. The RMIT journey covers everything from agreeing definitions, certifying reports and using data trustees. She also shares how to build happy teams and how through partnering with the university, they are better equipping graduates from the university with practical experience for workforce-ready employees. Tune in to hear more on her journey! Enjoy the show! Read the full episode summary here: Ep 156 Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In Part 2 of getting to know Felipe Flores, Felipe answers questions on what types of environments and challenges keep him energised, where he sees the data science industry heading in the next few years, and how job seekers can de-risk decision making for their potential employers. Felipe talks about: Using data to improve Data Futurology The gap between University and industry Which projects should you show to potential employers to demonstrate good foundational knowledge Intellectual capital vs money capital, Angel Investors and the approach to start and develop a business Strategies and decision making techniques to adapt to change MLOps – Felipe’s view on using Feature Stores when deploying models Read the full episode summary here: Ep 155 Enjoy the show! Thanks to our sponsor Talent Insights Group! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In part one of this special episode, Felipe reflects on the path his career has taken from his first start-up company to how he started Data Futurology and to his latest role in Healthcare.  In this open and honest conversation, he reveals the mistakes he made along the way and the lessons he learned from those failures. Successful people do have struggles, but you must be willing to ask for help. Passionate about his field, Felipe began the Data Futurology podcast in 2018 when he realised he needed a productive outlet for his obsession! Fast forward to today and he has interviewed over 200 guests from Australia and around the world from companies such as NASA, Formula 1, the AFL. And even a futurist! Listen in next week for part two as Felipe takes Q&A from the audience. Quotes:  There were so many things I did wrong during that time and so many things that I learned during that time, but it was the best training ground because the stakes were so high, and I felt like I had to be improving so quickly. It taught me a lot of lessons and a lot of what has helped to well in corporate is the lessons from my time as an entrepreneur. The first year was tough. There was a huge education piece. Nobody understood data science. Nobody really cared about data science. I would go and speak to business heads and executives let’s talk about what we can do for your area. But people did not take it very seriously. I’ve interviewed over 200 guests, including the CTO from Nasa, Martin Ford a futurist, that was super exciting. We have had people on the show from all different industries. It’s been awesome. Enjoy the show! Thanks to our sponsor: Talent Insights Group --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Pieter Vorster is the GM CSR & Chief Data Analytics Officer at Commonwealth Bank, one of the largest companies in Australia. He is an internationally recognized leader in the data analytics space with over 20 years of experience in industries like retail, wealth, corporate and investment banking, financial services, insurance, etc. In this episode, Pieter explains how he considers his real data journey started in 2013 when, with 2 colleagues, he had the opportunity to travel the world and meet with people who could help them in applying digital data design into corporate banking. Then, he talks about how disruption within the organizations, though sometimes avoided, is necessary to create better solutions for the customers. He believes a key guiding principle should be to benefit the client, and that transforming strategies and technologies can only take place at the same pace as social transformation. He provides his insight on the roles of CDO and CAO, both are positions he has held at different companies,  and how he sees that these may evolve in the future. Stay tuned to learn more about Pieter’s exciting journey and his ability to combine customer perspectives with data, AI and digital business models in a unique and effective way to get results for organizations. Quotes: “The moment you need to get agreement from everyone, mediocrity around consensus will apply” “If you’re just trying to move money from one part of the business to another part of the business you’ll never succeed” “I tried to convince some actuaries to learn about computer science and programming” “The banks have got lots of data, lots of toolsets, people just don’t know how to use them” “Logic never wins arguments, emotions win arguments” Read the full episode summary here: Ep 128 Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

The digital transformation of the retail industry has been going on for years. It has increased speed, efficiency, and accuracy across every branch of retail business, thanks in large part to advanced data and predictive analytics systems that are helping companies make data-driven business decisions. None of those insights would be possible without the internet of things (IoT), and most importantly, artificial intelligence. AI in retail has empowered businesses with high-level data and information that is leveraged into improved retail operations and new business opportunities. In this week’s podcast episode, Felipe speaks with Aaron Pratt. AI & Advanced Analytics Lead at Country Road Group. Aaron is currently the AI & Advanced Analytics Manager at Country Road Group and David Jones. He loves to uncover what it is that makes customers tick, and strives to use his data science expertise to provide the best possible customer experience to them. Having started his career life as a Games Designer with very strong mathematical prowess in the Gaming Industry, he quickly came up with a range of creative ideas based on a range of statistical models. This has led to a variety of AI use-cases, from facial recognition time-carding to fraud detection to omnichannel personalization, now implemented throughout a number of different retailers. Quotes: NPS is probably one of the key things that we, amongst many other organisations to start to understand sort of customer sentiment. Definition for price optimization. Basically, if you're selling a product, the one thing that you can control is the price point they sell it at. So if you're selling, say a jumper, or a T-shirt, or like whatever you're selling, the price point directly, is impacting the demand for that product. So the demand for the online like, when we went through COVID, I'm sure we weren't the only ones. But online just went absolutely through the roof. I think AI, relies on the BI to be right. Most of the time. So if you're wanting to get AI really embedded correctly, you need to make sure that you have your BI completely sorted out. Thanks to our sponsor: Talent Insights Group Read the full episode summary here: Ep 153 Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

As few as 15% of data scientists today are women. And that lack of diversity is a serious issue. AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences. What’s preventing more women from joining the field? In this week’s podcast episode, Felipe will be speaking with Fiona Milne, Head of Data and Ai at Whispir and Founder of WiMLDS Chapter Fiona is an Astrophysicist and Mathematician that heads the data and Ai at Whispir. With more than 10 years of experience in data science, Fiona offers Whispir invaluable experience on machine learning with previous projects for NAB, Odds.com.au, MYOB and the ACCC (Australian Competition and Consumer Commission). She has also founded the ‘Women in Machine Learning and Data Science’ Meetup alongside two industry colleagues. The group encourages and supports women in the industry through monthly events which include speakers, panels, evening and lunchtime events. With a Masters in Astrophysics, she also has spent time working on ‘passion projects’ which use machine learning to at images from Kepler telescope and leveraging Google AI’s open-source library Astronet to potentially discover new exoplanets. Quotes: Diversity is really important. Diverse teams are proven to be more creative and more innovative. And they're proven to be better at solving problems, they're proven to be better at making decisions. Biases or unfairness in the development of AI. And that kind of like helps us have a conversation where we get to decide the society that we want to create in the future, instead of blindly perpetuating the one that we've had so far. Knowing that there are improvements we can make there. If you are in a position to vote with your feet, go for it. We need visible leaders that come from different parts, that think differently, that is diverse in their genders and across the spectrum. Thanks to our sponsor Talent Insights Group Read the full episode summary here: Ep 152 Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this week’s episode, Felipe will be speaking with Simon Herbert, NSW Customer Service Department’s Chief Data Officer. Simon has worked for the NSW Data Analytics Centre for over three years. He has been instrumental in the transformation of the DAC to an agile culture. He and his team have built an advanced analytics service in the commercial cloud which delivers the enhanced capability and scalability to support the Customer Service department and NSW Government as a whole. Simon has over 20 years of experience in data, technology and transformation in many different countries including the UK, US, Singapore and Hong Kong. He has worked for companies such as Macquarie, Westpac, HSBC, IBM and Motorola. He was recognised as one of the top 20 CIOs in Australia as part of the 2019 and 2020 CIO50 awards. Quotes: It's very important that you have data governance outside of data science or data services, there is a Chinese wall to make sure that a steward can actually go up and say, no, you can't do that. The privacy Impact Assessments did not occur, which we cannot release. That's really important to have those kinds of controls in place. They have such a deep understanding of our behaviour and the way that they do that they're testing first their control groups and their rollout. But it's very scientific, and they can make a significant impact on a number of outcomes throughout the state. And so the application of deep learning definitely can give some significant benefits. But you do need to make sure that everybody understands the benefits. You haven't put any bias in and all the other things, we need to be very careful around deep learning. Thanks to our sponsor Talent Insights Group Read the full episode summary here: Ep 151 Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Today we have Apple’s Senior Data Scientist, Farhan Baluch as we deep dive into Data Analytics. Farhan has over 10 years of combined academic and industrial experience in machine learning. He likes to combine strategy with tactics and enjoy operating both at the micro and macro levels of data science efforts. As a neuroscientist, he worked on data obtained from sensors used to record neural activity from humans and insects. As a data scientist in the business world, he worked with data from large automotive, telecommunication and other industries as well as led small teams of data scientists at Opera solutions. In his role at Netflix, he brought data science to the world of entertainment and used quantitative methods to bring structure to the problem of predicting the success of content on Netflix. Currently, in his role at Apple, he’s working on a variety of projects aimed at personalizing the search ad experience within the App Store. Quotes: When I talk about effective data science, and we're specifically talking about data science, which is the combination of sort of machine learning, math, statistics, and in the business domain knowledge, it's purely doing sort of machine learning and AI for a particular purpose, which is to add value to your business. And so when I talk about effective data science, it's really about seeing in that sense, how do you come up with a process or a data science function that brings that value to your business. In terms of effective data science, I think you want each function to be doing, what their domain expertise is, and you want as little sort of cross-functional cooperation. Although not necessarily people leaning over onto the keyboard, right?  And I think that's where the effectiveness comes in. I think AV testing gives us that power to sort of scale customer feedback, without having to actually do interviews, though, anecdotal interviews do help. Thanks to our sponsor Talent Insights Group Read the full episode summary here: Ep 150 Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message