As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision.  John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network.  Solongo Erdenekhuyag is the former Customer Success and Data Strategy Leader at Exasol. Solongo is skilled in strategy, business development, program management, leadership, strategic partnerships, and management. In the episode, Richie, Solongo, and John cover the motivation for moving analytics to the cloud, economic triggers for migration, success stories from organizations who have migrated to the cloud, the challenges and potential roadblocks in migration, the importance of flexibility and open-mindedness and much more.  Links from the ShowExasolAmazon S3Azure Blob StorageGoogle Cloud StorageBigQueryAmazon RedshiftSnowflake[Course] Understanding Cloud Computing[Course] AWS Cloud Concepts



#150 Unlocking the Power of Data Science in the Cloud

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone.

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In almost every industry, the rate of innovation is increasing, and this is great for consumers around the globe. However, with constant innovation and continual disruption of the status quo, where to innovate next becomes much harder to identify. If your industry hasn’t been disrupted yet, it’s next on the list. So, in order to deal with uncertainty, a new culture is needed, and there’s a clear group of companies that constantly deal with uncertainty and innovation—VC’s.  Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and Professor of Finance at the Stanford Graduate School of Business, and a Research Associate at the National Bureau of Economic Research. He is an expert in corporate finance, venture capital, innovation financing, and financial decision-making. He is the founder and director of the Stanford GSB Venture Capital Initiative. In the episode, Richie and Ilya explore the venture mindset, the importance of embracing unknowns, how VC’s deal with unpredictability, how our education affects our decision-making ability, practical examples from Ilya’s teaching experiences at Stanford, adapting to market changes and continual innovation, venture mindset principles and much more.  Links Mentioned in the Show:Ilya’s WebsiteSequoia CapitalStanford University Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition
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#206 The Venture Mindset with Ilya Strebulaev, Economist & Professor at Stanford Graduate School of Business

Speedily adopting new technologies can give your business a competitive advantage, but with so much happening in the world of generative AI, it's difficult to know what to adopt. In this episode, Richie chats to two venture capitalists to get their view on the global AI landscape, where we are in the AI hype cycle, and how to adopt AI tech. Beyond this, we explore Rocketship.vc's use of data and algorithms to make investment decisions in early-stage startups. If our previous episode’s deep dive into 2024’s data & AI trends with VC Tom Tunguz got you excited about how investors are looking at the market at the moment, then this episode is sure to do the same. This time, we have twice the insight, thanks to our two guests. Madhu Shalini Iyer is a Managing Partner at Rocketship.vc, a Silicon Valley based fund investing globally. She was the Chief Data Officer of Gojek and helped grow the business into a $10 billion unicorn. In addition to being a board member, she started the Singapore office and played an active role in the strategy, new business development, and ‘data as a competitive advantage’. Prior to Gojek, Madhu was part of the founding team of Intuit’s Quickbooks Lending Platform. As the data science leader at Intuit, Madhu helped grow the platform to $300 million and holds 2 patents in the areas of user data augmented algorithms for financial inclusion. Madhu was also the Chief Data Officer for Ethoslending. There she built the underwriting platform and was responsible for all b2c revenue, resulting in $65 million gross market value per month. Madhu was further responsible for building and running the marketing team. Prior, Madhu was a partner at a $150m private equity fund, Stem Financial, in Hong Kong. She started her career as a senior data scientist with a leading think tank in Menlo Park, CA. Sailesh Ramakrishnan is also a Managing Partner at Rocketship.vc. Prior to Rocketship.vc, Sailesh was CTO and co-founder of LocBox (acquired by Square), a startup focussed on marketing for local businesses. Sailesh worked with Anand and Venky at their previous startup Kosmix, and continued on to Walmart as a Director of Engineering at @WalmartLabs. Before jumping into the startup world, Sailesh worked as a Computer Scientist at NASA Ames Research Center. Sailesh earned his Bachelors degree in Civil Engineering from IIT Madras, his Masters degree in Construction Management from Virginia Tech and another Master degree in Intelligent Systems from University of Pittsburgh. He was a Ph.D. candidate in Artificial Intelligence at the University of Michigan. In the episode, Richie, Madhu and Sailesh explore the generative AI revolution, categorizing generative AI tools, the impact of genAI across industries, investment philosophy and data-driven decision-making, the challenges and opportunities when investing in AI, future trends and predictions, regulatory and ethical considerations of AI, and much more.  Links Mentioned in the Show:Rocketship.vc[Course] Implementing AI Solutions in BusinessRelated Episode: Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche BankSign up to RADAR: AI Edition
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#205 The 2nd Wave of Generative AI with Sailesh Ramakrishnan & Madhu Iyer, Managing Partners at Rocketship.vc

Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC.  Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.  In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups. Links Mentioned in the Show:Tom’s BlogTheory VenturesArticle: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case[Course] Implementing AI Solutions in BusinessRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition
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#204 Data & AI Trends in 2024, with Tom Tunguz, General Partner at Theory Ventures

With seemingly every organization wanting to enhance their AI capabilities, questions arise about who should be in charge of these initiatives. At the moment, it’s likely a CTO, CIO, or CDO, or a mixture of the three. The gold standard is to have someone in the C-suite whose sole focus is their AI projects: the Chief AI Officer. This role is so new that it's not yet widely understood. In this episode, we explore what the CAIO job entails. Philipp Herzig is the Chief AI Officer at SAP. He’s held a variety of roles within SAP, most recently SVP Head of Cross Product Engineering & Experience, however his experience covers intelligent enterprise & cross-architecture, head of engineering for cloud-native apps, a software development manager, and product owner.  In the full episode, Richie and Philipp explore what his day-to-day responsibilities are as a CAIO, the holistic approach to cross-team collaboration, non-technical interdepartmental work, AI strategy and implementation, challenges and success metrics, how to approach high-value AI use cases, insights into current AI developments and the importance of continuous learning, the exciting future of AI and much more.  
 Links Mentioned in the Show:SAP’s AI CoPilot JouleSAP[Course] Implementing AI Solutions in BusinessRelated Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at WalmartRewatch sessions from RADAR: The Analytics Edition

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#203 How a Chief AI Officer Works with Philipp Herzig, Chief AI Officer at SAP

Countless companies invest in their data quality, but often, the effort from their investment is not fully realized in the output. It seems like, despite the critical importance of data quality, data governance might be suffering from a branding issue. Data governance is sometimes looked at as the data police, but this is far from the truth. So, how can we change perspectives and introduce fun into data governance? Tiankai Feng is a Principal Data Consultant and Data Strategy & Data Governance Lead at Thoughtworks, He also works part-time as the Head of Marketing at DAMA Germany. Tiankai has had many data hats in his career—marketing data analyst, data product owner, analytics capability lead, and data governance leader for the last few years. He has found a passion for the human side of data—how to collaborate, coordinate, and communicate around data. TIankai often uses his music and humor to make data more approachable and fun. In the episode, Adel and Tiankai explore the importance of data governance in data-driven organizations, the challenges of data governance, how to define success criteria and measure the ROI of governance initiatives, non-invasive and creative approaches to data governance, the implications of generative AI on data governance, regulatory considerations, organizational culture and much more.  Links Mentioned in the Show:Tiankai’s YouTube ChannelData Governance Fundamentals Cheat Sheet[Webinar] Unpacking the Fun in Data Governance: The Key to Scaling Data Quality[Course] Data Governance ConceptsRewatch sessions from RADAR: The Analytics Edition
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#150 Unlocking the Power of Data Science in the Cloud

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