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

Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016. In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams.  Enjoy the show! We speak about: [01:20] How Tony got started in data [08:20] Tony’s skills come from the commercial side [11:10] Linking data science and the business [14:30] Communicating how data science works [17:00] Steps to getting others to understand data science [20:40] Getting the best talent for your team [24:00] Structuring teams and the department [28:10] Transiting from analytical roles to commercial roles  [35:30] Working on global expansion [38:10] Solving with artificial intelligence [42:30] Passionate about using numbers to reach an outcome [44:00] Modelling failures with Sportsbet  [47:50] Imposter syndrome in data science   [50:05] Data science is rapidly changing and exciting Resources: Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/ Sportsbet: https://www.sportsbet.com.au Tony’s Twitter: https://twitter.com/gruebz?lang=en Quotes: “There is no one path that always works.” “There are literally thousands of things data scientists couldn’t potentially tackle in any business.” “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.” “Not having imposter syndrome is a sign of lack of knowledge.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.  Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.  Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.  Enjoy the show! We speak about: [03:40] Large companies and their biases  [05:40] Racism and sexism is in our data [08:45] Uncovering inputs of the bias    [10:45] Unstructured data approaches  [14:30] Using ELI5  [19:20] The right to an explanation  Quotes: “We teach our algorithms on how to replicate our decisions.” “The algorithms show the inequality that we have in the world today.” “Explainable AI is more ethical in the sense that it is more transparent.” “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.” Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language.  In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined.  Enjoy the show! We talk about: [01:40] How Yuval fell in love with data science [05:45] Social learning in biology [08:05] Lessons learned from completing a Ph.D. [13:10] Research innovation vs. solving problems [15:40] Embrace simplicity  [18:00] Small business advantages  [21:45] Skills to develop before management  [26:00] Results oriented work [30:45] Different flavors of management [32:50] Connection to community  [40:20] Learning to interact with stakeholders + managerial skills  [44:00] Benefits of building connections + education  [48:00] Assume people are at work with good intentions  [52:00] Allocate time for professional development  [59:30] Focus on retention Resources: Data Science Melbourne  MelbURN Yuval’s LinkedIn University of New South Wales Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future. In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.  Enjoy the show! We speak about: [02:10] How Eric started in the technology space [05:15] Moving from one career path to another [09:50] Eric’s most significant failure as an investor  [13:30] Picking the timing   [18:15] AI is larger than what currently exists   [21:30] Embracing the technology behind AI [29:45] Hurdles for companies who are adopting AI   [41:30] Reactions from people learning about AI  [48:40] Shortage of truck drivers + how technology is making driving easier  [54:00] AI in the medical field  [61:30] Using a categorical approach   Resources: Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/ Eric’s Twitter: https://twitter.com/ead  Website: http://conexus.ai/ Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.  In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.  Enjoy the show! We speak about: [02:50] Martin’s background  [05:45] The themes behind Martin’s writing  [08:35] Machine learning is when algorithms can make decisions   [12:00] Amazon is susceptible to automation  [16:45] The most common occupation error is driving some kind of vehicle    [18:15] The type of work that will be left for humans   [21:45] Universal basic income   [28:55] Building explicit incentives to earn more income; paying people more to pursue education [33:25] Artificial intelligence will be the primary force shaping our futures  [38:35] The solution is not to teach everyone how to code  [41:30] Architects of Intelligence: The truth about AI from the people building it [46:00] Deep learning is the biggest thing to happen to artificial intelligence   [52:20] Controlling data and an entirely new industry called data banks  [53:15] Negative implications of artificial intelligence  [64:40] You do not want to be doing something predictable  Resources: Martin’s Website: https://mfordfuture.com/about/ Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/ Martin’s Twitter: https://twitter.com/MFordFuture TED Talk: https://www.ted.com/talks/martin_ford Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements. In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.  Enjoy the show! We speak about: [01:20] How Annie got into the world of data [10:00] Insight for people starting in the data space [12:50] Organizations are not predictable   [14:50] Annie’s team at ME Bank  [27:50] Turning recruitment on its head [33:20] Transitioning from teaching to general manager  [39:05] Sort out your personality and experiment with leadership  [46:30] Imposter syndrome   [49:10] Experimenting with diversity in the workforce  [53:30] Challenges with discrimination in the workplace    [61:10] Define yourself; do not be defined by others  Resources: Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/ ME Bank: https://www.mebank.com.au IT Jobs Watch: https://www.itjobswatch.co.uk Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.  In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.  Enjoy the show! We speak about: [01:50] How Pavel started in the data space  [09:50] Vietnam is an ideal space for working remotely and teaching English  [21:40] The moment Pavel found Kaggle  [24:20] How Pavel became a data scientist  [28:00] Difference between machine learning engineers and researcher data scientists  [31:50] Why is it essential to be the very best? [34:20] Machine learning and mathematics  [36:45] The early days of Pavel’s Kaggle journey  [40:00] Pavel’s favorite part of Kaggle  [47:20] The role of automation in Kaggle    [49:40] The steps when approaching a new Kaggle competition  [52:55] Think twice before you commit to data science  Resources: Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru Pavel’s Kaggle: https://www.kaggle.com/ppleskov Pavel’s Twitter: https://twitter.com/ppleskov Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective. In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.  Enjoy the show! We speak about: •    [01:45] How Danielle started in the data space  •    [03:20] Background and career   •    [06:20] Deciding what problems to tackle first on the job  •    [08:35] Evolution of marketing    •    [13:35] Favorite failure •    [16:50] How to communicate data •    [18:30] Visual presentation style  •    [19:45] How Danielle creates a story  •    [21:30] How do you structure visuals for executives? •    [23:10] How do you think people can get better at this skill? •    [24:45] What is a strategist for data? •    [27:40] What is the role outside of data? •    [29:00] The main challenges for Danielle’s clients •    [32:30] Working with clients on case-by-case basis •    [33:30] Qualities of a great data scientist   •    [35:30] What do you think makes a good data leader?  •    [36:15] Current challenges in the data space •    [37:40] Future challenges for the data space •    [42:40] Advice for future data scientists and leaders Resources: Sexy Little Numbers Free Range Creatives: https://www.freerangecreatives.co.za/ Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Peter Elger is the founder and CEO of fourTheorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them. In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.  Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.  Enjoy the show! We speak about: [01:45] How Peter started in the data space  [06:50] Transition to disaster recovery   [08:55] Interactive radio and marketing applications  [13:40] Maintaining a grip with technical skills  [16:20] The entrepreneurial bug came organically to Peter [18:40] Transition to entrepreneurship   [21:45] What to look for in a co-founder  [26:00] Building analytics with machine learning  [29:00] A tale of two technologies  [33:00] Applying AI to existing platforms  [35:10] Knowledge of AI is not necessary to use AI as a service   [37:50] Capable team members are difficult to find  [40:10] Sharing management meetings with all staff members   [44:05] Experiences with handling politics in organizations  [48:50] Removing ego + allowing the team to do their best work  [50:30] Scheduling work to maximize the impact  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.  In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.  If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into. Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.  Enjoy the show! Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60. In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.  Enjoy the show! We speak about: [01:45] How Valeriy started in the data space  [06:10] Transiting to working at a bank [11:30] Understanding the business process  [15:10] Gaining trust from clients  [20:20] Data scientists are business analysts  [24:10] Expectations from the job interview  [25:50] Starting data science teams [31:40] The type of mindsets to look for in a team member  [37:30] Different teams complement each other  [40:20] Valeriy’s journey with Kaggle [47:40] Ethical challenges in the industry  [51:20] Persistence is key Resources: Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/ Valeriy’s Kaggle: https://www.kaggle.com/venheads Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.  In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for. Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.  Enjoy the show! Resources: Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a Digital-Dandelion: https://www.digital-dandelion.com Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations. In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.  Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.  Resources: Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/ Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.  In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them. We speak about: [01:30]      How Jonny started in the data space  [04:50]      Public relations [06:00]      Descriptive, prescriptive, and predictive  [08:15]      Difference between interesting and useful [10:00]      Understanding the customer [15:25]      Cultural shift of data in organizations  [19:10]      Challenging the status quo  [22:40]      Shiny object syndrome  [26:45]      The twenty percent time [30:00]      Bringing data application to the masses [34:30]      Each stage of the customer journey  [39:30]      Getting value for money [42:45]      Return on investment  [44:15]      Data + creativity  Resources: Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics. In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground. Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports. Enjoy the show! Show Notes: • [02:20] How Warwick started in data science • [05:55] Aptitude for research • [08:40] Purpose-built software + decision trees • [12:20] Accepting opaque solutions vs. transparent solutions • [16:45] Pushback of data analytics • [21:15] Difference between good support and bad support on the job • [25:25] Necessity to learn the domain first • [29:00] How to learn on the job • [32:20] Process of publishing research • [41:50] Improving legal and privacy concerns • [44:25] Automated support + decision-making operations • [52:40] Developing an analytical + practical mindset • [58:10] Hyperspecialized • [64:30] Moving toward data + analytics as a service • [66:25] Advice from Warwick Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message