Why Your Model Gets it Wrong | Dejan Mitkovski (BetMGM)

Why Your Model Gets it Wrong | Dejan Mitkovski (BetMGM)

In this episode of The Data Storytellers Podcast, we speak with Dejan Mitkovski, a data and technology leader whose career has spanned Microsoft, Amazon, Google, FanDuel, and BetMGM. Dejan reflects on the last 25 years of digital transformation, from the early days of relational databases and BI tools to the rise of big data, data science, machine learning, and AI. We explore how data leadership has evolved from reporting to prediction, why legacy organizations struggle to adopt new technologies, and how leaders can prove value through small, practical tests. Dejan also discusses the difference between traditional machine learning and AI, the importance of data quality and governance, and why curiosity, discomfort, and people leadership remain central to building successful data organizations. Connect with us: Website: https://thedatastorytellers.com/ [https://thedatastorytellers.com/]LinkedIn: https://www.linkedin.com/company/the-data-storytellers [https://www.linkedin.com/company/the-data-storytellers]Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 [https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476]Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa [https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa]YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA [https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA] Chapters: 00:00: Introduction and Dejan’s return to Vancouver 03:43: The 25-year arc of data, digital transformation, and AI 04:26: Early influences, science, and choosing a different career path 09:40: First exposure to analytics and the move into Microsoft 12:20: Relational databases, Hadoop, and the early digital era 16:07: Stitching data together, personalization, and the rise of BI 20:38: Big data, data science, and the shift from reporting to prediction 29:35: Amazon, mentorship, forecasting, and thinking like an owner 37:02: Google, legacy industries, and the challenge of changing how companies work 42:35: Proving value through small tests and cultural adoption 50:45: Machine learning, measurement, and what makes AI different 59:41: FanDuel, data products, governance, and scaling a data organization 1:10:51: ChatGPT, generative AI, and the limits of messy data 1:18:18: BetMGM, AI enablement, and lessons for data leaders 1:21:58: Curiosity, discomfort, and the human side of leadership

What "Data-Driven" Actually Means | Anthony Jackel (Ferrara)

What separates analytics teams that get a seat at the table from those stuck in the reporting queue? Anthony Jackel, Senior Director of Business Intelligence & Analytics at Ferrara, has spent his career answering that question, from Kraft to one of America's largest candy companies. In this conversation, we explore: * What "data-driven enterprise" actually means * The shift from data providers to decision enablers * Why user-centered design matters as much for dashboards as it does for iPhones * How to build analytics products that drive action, not just curiosity * The mindset shift that earns analytics a seat at the table Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters: 00:00 – Introduction to Anthony Jackel and his role at Ferrara 03:35 – Building Ferrara’s analytics team from scratch 07:42 – The problem with dashboards and low-value data work 11:50 – Connecting analytics to business decisions and revenue 16:25 – The importance of commercial empathy for data leaders 20:18 – Getting out of the service provider mindset 25:04 – Translating technical insights for non-technical audiences 28:45 – Why building trust is more important than being right 33:29 – Lessons from finance that shaped his analytics approach 37:12 – Coaching and developing high-performing analytics talent 41:05 – The challenge of balancing curiosity with execution 45:18 – Internal marketing and the power of repeatable wins 49:56 – Final advice for future analytics leaders 📍 Chapter Timestamps (Finalized for 50:13 runtime)

AI Optimism vs. Evidence in Late 2025 | Ylan Kazi (Blue Cross Blue Shield North Dakota)

In this episode of The Data Storytellers Podcast, we sit down with Ylan Kazi, Chief Data & AI Officer at Blue Cross Blue Shield of North Dakota, to cut through the noise around enterprise AI. Ylan explains why early machine learning hype never matched operational reality, how LLMs changed the game once they became accessible to anyone, and why most AI failures stem from cultural friction rather than technical limitations. He breaks down the gap between expectations and actual ROI, the overlooked complexity behind agentic AI, and the fundamental difference between inserting AI into old workflows and redesigning processes to be AI native. We also explore the economic and societal contours of the current AI cycle, including energy constraints, the cultural backlash against AI generated content, and why exponential progress looks slow until it doesn’t. Ylan shares what the coming micro bubbles might look like, how labor markets are shifting, and why new technology forces each of us to examine the meaning of human work. We end with a look at his AI Edge newsletter and what he is tracking as this transformation accelerates. Chapters: 00:00 Introductions and Ylan’s early AI skepticism 04:30 Traditional AI vs generative AI and why prompting skill matters 08:00 Why enterprise ROI lags and why most failures are cultural 12:00 The economics of LLMs, energy constraints, and infrastructure realities 18:00 Agentic AI, undocumented processes, and why value requires redesign 25:00 Automation, talent pipelines, and shifts in labor markets 34:00 Cultural reactions to AI content and the value of human creation 45:00 The case for micro bubbles and faster boom bust cycles 57:00 Ylan’s newsletter, what he is writing about, and closing thoughts

The Hard Truths of Enterprise AI Adoption | Jodi Blomberg (Cox Automotive)

In this episode of The Data Storytellers Podcast, we speak with Jodi Blomberg, VP of AI and Machine Learning at Cox Automotive. Jodi shares her experience leading AI transformation at scale in a highly regulated, legacy-rich environment. We explore how to balance long-term R&D with immediate impact, why trust and humility are critical in AI leadership, and how to move from proofs of concept to deployed products that make a real difference. Jodi also discusses the cultural and operational shifts required to truly embed AI in the enterprise and her own career journey from econometrics to machine learning. Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters: 00:00 – Introduction and Jodi Blomberg’s role at Cox Automotive 02:44 – The mission of the AI and Machine Learning team 06:12 – Where AI is driving value in automotive ecosystems 10:25 – Lessons learned leading data teams in legacy orgs 14:01 – Balancing long-term R&D with business impact 18:17 – Why successful AI teams don’t chase shiny objects 22:39 – The cultural change required for enterprise AI 26:15 – From proof of concept to production in a regulated space 30:34 – Jodi’s career path from econometrics to applied AI 35:48 – The role of humility and empathy in AI leadership 40:09 – Bringing executive stakeholders on the journey

The AI Leadership Playbook for Global Enterprises | Elena Alikhachkina

In this episode of The Data Storytellers Podcast, we speak with Elena Alikhachkina, Chief Data and AI Officer at TE Connectivity. With a background spanning Pfizer, Johnson & Johnson, Nestlé, and now one of the world’s largest industrial tech companies, Elena shares what it takes to lead AI transformation across global, complex organizations. We talk about the realities of operationalizing data and AI in manufacturing, the soft skills required to drive trust and adoption, and how to move from use cases to enterprise value. Elena also reflects on how she built her career across industries, why curiosity is her superpower, and how data leaders can move from technical execution to strategic leadership. Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters: 00:00 – Introduction and Elena’s current role at TE Connectivity 02:41 – Why AI transformation is a leadership challenge, not a tech one 06:50 – How to build trust across functions when driving change 10:22 – The power of storytelling in operational environments 14:36 – Creating a data strategy that scales globally 18:58 – Why speed alone is not a data strategy 23:11 – Aligning AI with core business priorities 27:04 – Moving from isolated use cases to enterprise-wide value 31:17 – Lessons from Nestlé and Johnson & Johnson 36:03 – Defining value clearly in industrial use cases 40:40 – The evolution of the Chief Data and AI Officer role 44:12 – Building multidisciplinary teams that earn business trust 49:18 – Advice for data leaders entering legacy-heavy environments 53:46 – Why curiosity, empathy, and resilience matter most 58:03 – How Elena built her career across industries and functions 1:03:40 – What’s next in data and AI at TE Connectivity 1:08:12 – Closing thoughts on leadership, impact, and lifelong learning