Breaking Data Silos: AI-Ready Data Strategies with Nishith Trivedi, Enterprise Data Governance and Global MDM Lead at Pfizer

Data Hurdles

44:18

AI and Generative AI are transforming cybersecurity by enhancing threat detection and response. These technologies offer unmatched accuracy and efficiency, making them crucial for protecting sensitive data. As cyber threats evolve, integrating AI into security strategies is essential. This week, Dave, Esmee and Rob talk to Corence Klop, CISO at the Rabobank, about the expanding role of AI and Generative AI in cybersecurity, and how to begin integrating these technologies into your organization. TLDR 04:45 Rob is confused about wrong AI information for a hotel booking 08:20 Conversation with Corence 33:40 How can you identify the state of flow for your end-user in agile practices? 40:50 Going to the swimming pool and disco with your daughter Guest Corence Klop: https://www.linkedin.com/in/corenceklop/ Hosts Dave Chapman: https://www.linkedin.com/in/chapmandr/ Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ Production Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/ Dave Chapman: https://www.linkedin.com/in/chapmandr/ Sound Ben Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/ Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ ' Cloud Realities' is an original podcast from Capgemini…

18天之前 43:36

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Nishith provides a fascinating look at how a pharmaceutical giant manages data across multiple verticals—from supply chain to R&D—while explaining the challenges of making data "AI-ready." He discusses the evolution from vector-based RAG to graph-based approaches, the importance of ontologies in preventing AI hallucinations, and how knowledge graphs help connect unstructured data.

The conversation explores how Pfizer is navigating complex regulatory requirements across 150+ countries, the shift toward patient-centric approaches, and the vision for creating FAIR data (Findable, Accessible, Interoperable, and Reusable). Listeners will gain valuable insights into enterprise data governance, the future of agentic AI, and practical strategies for breaking down data silos in large organizations.

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Data Hurdles

Breaking Data Silos: AI-Ready Data Strategies with Nishith Trivedi, Enterprise Data Governance and Global MDM Lead at Pfizer

Data Hurdles

published 18天之前

MP3•单集首页

49集单集

All episodes

Data Hurdles

1
The Shield, Not the Weapon: Ethical AI Surveillance with Ram Bulusu of Warp9Ai 40:13

4天之前40:13

40:13

In this thought-provoking episode of Data Hurdles, hosts Chris Detzel and Michael Burke speak with Ram Bulusu, Head of Applied Artificial Intelligence of Warp9Ai about his work developing advanced surveillance technologies for public safety applications. The conversation primarily explores Ram's development of an AI-enabled camera system designed for airports and border crossings that uses multimodal data inputs to identify potential security threats in real-time. Ram explains his concept of "benevolent monitoring" - using AI surveillance as a protective shield rather than a controlling weapon - and details how his proposed system could help prevent security breaches, traffic accidents, and crimes by detecting behavioral patterns before incidents occur. He discusses the technical challenges of creating real-time monitoring systems, including energy requirements and data management issues, while addressing concerns about privacy and government oversight. The discussion also touches on Ram's other AI projects, including an interactive AI psychotherapist designed to provide immediate mental health support for those in crisis. Throughout the episode, hosts Chris and Mike raise thoughtful questions about the ethical implications, privacy concerns, and potential benefits of these emerging surveillance technologies, creating a balanced exploration of how AI might transform public safety and security in the coming years.…

Data Hurdles

1
Breaking Data Silos: AI-Ready Data Strategies with Nishith Trivedi, Enterprise Data Governance and Global MDM Lead at Pfizer 43:36

18天之前43:36

43:36

In this insightful episode of Data Hurdles, hosts Chris Detzel and Michael Burke sit down with Nishith Trivedi, Enterprise Data Governance and Global MDM Lead at Pfizer. Nishith shares his journey from chemical engineering to becoming a data expert, and details how his team is transforming Pfizer's data landscape to support AI initiatives. Nishith provides a fascinating look at how a pharmaceutical giant manages data across multiple verticals—from supply chain to R&D—while explaining the challenges of making data "AI-ready." He discusses the evolution from vector-based RAG to graph-based approaches, the importance of ontologies in preventing AI hallucinations, and how knowledge graphs help connect unstructured data. The conversation explores how Pfizer is navigating complex regulatory requirements across 150+ countries, the shift toward patient-centric approaches, and the vision for creating FAIR data (Findable, Accessible, Interoperable, and Reusable). Listeners will gain valuable insights into enterprise data governance, the future of agentic AI, and practical strategies for breaking down data silos in large organizations.…

Data Hurdles

1
DeepSeek's Cost-Efficient Model Training ($5M vs hundreds of millions for competitors) 24:42

6 weeks之前24:42

24:42

The episode features hosts Chris Detzel and Michael Burke discussing DeepSeek, a Chinese AI company making waves in the large language model (LLM) space. Here are the key discussion points: Major Breakthrough in Cost Efficiency: - DeepSeek claimed they trained their latest model for only $5 million, compared to hundreds of millions or billions spent by competitors like OpenAI - This cost efficiency created market disruption, particularly affecting NVIDIA's stock as it challenged assumptions about necessary GPU resources Mixture of Experts (MoE) Innovation: - Instead of using one large model, DeepSeek uses multiple specialized "expert" models - Each expert model focuses on specific areas/topics - Uses reinforcement learning to route queries to the appropriate expert model - This approach reduces both training and inference costs - DeepSeek notably open-sourced their MoE architecture, unlike other major companies Technical Infrastructure: - Discussion of how DeepSeek achieved results without access to NVIDIA's latest GPUs - Highlighted the dramatic price increase in NVIDIA GPUs (from $3,000 to $30,000-$50,000) due to AI demand - Explained how inference costs (serving the model) often exceed training costs Chain of Thought Reasoning: - DeepSeek open-sourced their chain of thought reasoning system - This allows models to break down complex questions into steps before answering - Improves accuracy on complicated queries, especially math problems - Comparable to Meta's LLAMA in terms of open-source contributions to the field Broader Industry Impact: - Discussion of how businesses are integrating AI into their products - Example of ZoomInfo using AI to aggregate business intelligence and automate sales communications - Noted how technical barriers to AI implementation are lowering through platforms like Databricks The hosts also touched on data privacy concerns regarding Chinese tech companies entering the US market, drawing parallels to TikTok discussions. They concluded by discussing how AI tools are making technical development more accessible to non-experts and mentioned the importance of being aware of how much personal information these models collect about users.…

Data Hurdles

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Clean Data, Business Context, and the Future of Analytics - Featuring Noy Twerski, Sherloq Co-founder & CEO 33:46

7 weeks之前33:46

33:46

This episode of Data Hurdles features an in-depth conversation with Noy Twerski, CEO and Co-founder of Sherloq, a collaborative SQL repository platform. The discussion, hosted by Chris Detzel and Michael Burke, explores several key themes in data analytics and management. Key Topics Covered: 1. Introduction to Sherloq - Sherloq is introduced as a plugin that integrates with various SQL editors including Databricks, Snowflake, and JetBrains editors - The platform serves as a centralized repository for SQL queries, addressing the common problem of scattered SQL code across organizations 2. Origin Story - Twerski shares her background as a product manager who experienced firsthand the challenges of managing SQL queries - The company was founded about 2.5 years ago with her co-founder Nadav, whom she knew from computer science undergrad - They identified the problem through extensive user research, finding that 80% of data analysts struggled with locating their tables, fields, and SQL 3. Business Context and AI Discussion - A significant portion of the conversation focuses on the relationship between SQL, business context, and AI - The hosts and guest discuss the challenges of automating SQL generation through AI, emphasizing the importance of business context - They explore why text-to-SQL solutions are more complex than they appear, particularly in enterprise settings 4. Future Outlook - Discussion of Sherloq's future plans, focusing on deepening their collaborative SQL repository capabilities - Exploration of how the platform could serve as infrastructure for future AI capabilities - Consideration of data quality as an ongoing challenge in the enterprise data space 5. Industry Insights - The conversation includes broader discussions about data quality, governance, and the evolution of data teams - Twerski shares insights about different user personas and how they approach the product differently Notable Aspects: - The podcast includes interesting perspectives on the future of data analytics and AI - There's a strong emphasis on practical business applications and real-world challenges - The hosts and guest share thoughtful insights about data quality as a persistent challenge in the industry The episode provides valuable insights for data professionals, particularly those interested in data management, SQL development, and the evolution of data tools in an AI-driven landscape.…

Data Hurdles

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Top 10 MDM 2025 Platforms - Who's Rising, Who's Falling & Why It Matters 1:07:23

18 weeks之前1:07:23

1:07:23

The Data Hurdles Impact Index (DHII) provides a comprehensive analysis of the top Master Data Management platforms, evaluating vendors based on multi-domain capabilities, core features, AI enablement, data governance integration, architecture flexibility, total cost of ownership, market reach, and vendor stability. This inaugural DHII analysis covers ten leading MDM platforms that are shaping enterprise data management in 2025. The assessment, led by 20-year MDM veteran Rohit Singh Verma , Director - Data practice, Nvizion Solutions , examines market leaders and emerging players including Informatica, Stibo Systems, Profisee, Reltio, Ataccama, TIBCO EBX, IBM Infosphere MDM, SAP MDM, Syndigo, and Viamedic. Each vendor is evaluated through the lens of practical implementation experience, market presence, and technological innovation. Key findings reveal Informatica's continued dominance with their IDMC cloud offering, though facing increasing pressure in specific domains from specialists like Stibo Systems in product data management. The analysis highlights a significant market opportunity in the Middle East, where only select vendors have established strong presences. The DHII also identifies critical factors beyond technical capabilities, including the importance of system integrator networks, implementation speed, and regional market penetration. The evaluation exposes interesting market dynamics, such as the challenges faced by legacy vendors like IBM and SAP in keeping pace with cloud-native solutions, and the emergence of AI-enabled capabilities as a key differentiator. The analysis also addresses the persistent challenge of high implementation failure rates (estimated at 75%) and how vendors are evolving to address this through improved user interfaces, AI-assisted implementations, and stronger partner ecosystems. This groundbreaking DHII assessment serves as an essential guide for organizations navigating the complex MDM vendor landscape, offering insights that go beyond traditional analyst evaluations to provide a practical, implementation-focused perspective on the market's leading solutions.…

Data Hurdles

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The Future of Data Teams in the AI Era: Insights from Alex Welch, dbt Labs' Head of Data and Analytics 50:58

22 weeks之前50:58

50:58

In this insightful episode of Data Hurdles, hosts Chris Detzel and Michael Burke sit down with Alex Welch, Head of Data at dbt Labs, to explore the transformative impact of AI on data organizations and the future of analytics. With over a decade of experience in FinTech and now leading data initiatives at dbt Labs, Alex shares valuable perspectives on: • Data Quality & Governance: - The critical importance of establishing data quality frameworks - How to approach data governance without creating unnecessary friction - The balance between control and accessibility in data management • AI Implementation & Challenges: - Two major hurdles in AI adoption: data/tech debt and the skills/culture gap - Practical approaches to introducing AI into existing workflows - The importance of starting small rather than trying to "boil the ocean" • Future of Data Teams: - Emerging roles like prompt engineering specialists and AI ethics officers - The shift from hierarchical structures to dynamic pod-based teams - How human-AI collaboration will reshape organizational structures • Skills & Development: - Why traditional analytical skills remain crucial in the AI era - The importance of maintaining human judgment and expertise - How to prepare for an AI-augmented workplace The conversation takes an especially interesting turn when discussing practical applications of AI, including Alex's personal example of using AI for meal planning and grocery shopping automation. The hosts and guest also explore thought-provoking perspectives on maintaining human expertise while leveraging AI capabilities, emphasizing the importance of using AI to augment rather than replace human decision-making. The episode concludes with valuable insights about preparing organizations for emerging AI trends and the importance of considering security implications in an AI-enabled future. This episode is particularly relevant for: - Data leaders planning AI initiatives - Organizations navigating data quality challenges - Professionals interested in the future of data careers - Anyone looking to understand the practical implications of AI in business…

Data Hurdles

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Data Mesh in Action: Challenges, Opportunities, and Real-World Examples with Willem Koenders 42:27

27 weeks之前42:27

42:27

In this comprehensive episode of Data Hurdles, hosts Chris Detzel and Michael Burke engage in a deep and insightful conversation with Willem Koenders, a global data strategy leader at ZS Associates, about the increasingly popular concept of data mesh. The episode begins with Willem providing his background and expertise in the data field, setting the stage for a rich discussion. He explains the core concept of data mesh, describing it as a domain-driven approach to data architecture that emphasizes decentralized ownership and governance of data across an organization. Throughout the conversation, Willem uses various analogies to make the concept more accessible, likening data mesh to a net with strategic data nodes, and comparing data assets to real estate properties that need proper management and care. These analogies help illustrate the shift from centralized data warehouses or lakes to a more distributed, domain-oriented approach. The hosts and guest delve into the challenges of implementing data mesh, including cultural shifts required within organizations. Willem emphasizes the importance of clear ownership, quality control, and the need for a product-oriented mindset when it comes to data assets. He discusses how data mesh can help solve long-standing issues of data quality and accessibility that many organizations face. Real-world examples and case studies are shared, providing listeners with practical insights into how data mesh principles are being applied across various industries. Willem talks about the financial sector's early adoption of similar concepts and how medical technology companies are now embracing data mesh to deal with evolving market demands and data-generating products. The conversation also covers the critical aspect of data governance in a mesh environment. Willem explains how governance needs to be balanced between centralized standards (especially for security) and domain-specific controls. He stresses the importance of enablement and providing the right tools for domain teams to manage their data effectively. Chris and Michael bring up the challenges of cross-functional collaboration and the often siloed nature of data work in organizations. Willem acknowledges these difficulties and discusses strategies for improving communication and alignment between different teams and roles. The episode explores how to measure the business impact of data mesh implementations. Willem advocates for a portfolio approach, where organizations track the value generated by specific data assets and their associated use cases, rather than focusing solely on technology investments. Looking to the future, the discussion touches on the potential for data mesh to become a dominant data architecture approach, especially for larger and more complex organizations. Willem expresses hope that evolving tools and technologies, including AI, will make data mesh implementation more accessible to a broader range of companies. Throughout the episode, the hosts and guest maintain a balanced view, acknowledging both the potential benefits and the significant challenges of adopting a data mesh approach. They emphasize that success depends not just on technology, but on organizational culture, trust, and effective communication. The conversation concludes with reflections on the importance of building trust between different parts of an organization and how frameworks like data mesh can facilitate better collaboration and data utilization when implemented thoughtfully. This episode provides listeners with a comprehensive overview of data mesh, blending theoretical concepts with practical insights and real-world examples. It offers valuable perspectives for data professionals, business leaders, and anyone interested in modern data architecture and management strategies.…

Data Hurdles

1
Revolutionizing Healthcare Data Sharing: Shubh Sinha, Integral's CEO, on Data Hurdles 27:20

34 weeks之前27:20

27:20

In this enlightening episode of "Data Hurdles," hosts Chris Detzel and Michael Burke engage in a deep conversation with Shubh Sinha, CEO and co-founder of Integral, about revolutionizing healthcare data sharing. Sinha, leveraging his experience at LiveRamp and his current leadership role at Integral, offers valuable insights into the intricate world of regulated data in healthcare. He elucidates how data fragmentation across various healthcare touchpoints creates significant challenges in comprehending a patient's complete journey. Sinha emphasizes the crucial balance between utilizing comprehensive patient data—encompassing both medical and non-medical information—and adhering strictly to evolving privacy regulations such as HIPAA, CCPA, and GDPR. The discussion explores Integral's innovative approach to these challenges, showcasing how their technology automates risk assessment and compliance checks for data sets, facilitating faster and more secure data sharing between healthcare entities. Sinha underscores the importance of proactive compliance in an increasingly regulated data landscape and how Integral's solutions are designed to swiftly adapt to new regulations. The conversation also addresses the impact of AI and large language models in the healthcare data space, highlighting new considerations such as bias in training data and the necessity for explainable AI in medical decision-making. As co-founder, Sinha provides a forward-looking perspective on the future of healthcare data, predicting a trend towards more regulated data across industries and positioning Integral as a vital link between compliance and data stacks. He envisions a future where data utility and privacy coexist harmoniously, fostering trust between healthcare providers and patients. The episode concludes with reflections on the growing importance of auditability and explainability in data-driven decisions, underscoring Integral's role in shaping a more transparent and efficient healthcare data ecosystem. This insightful discussion offers listeners a comprehensive understanding of the current challenges and innovative solutions in healthcare data sharing, highlighting how companies like Integral, under Sinha's co-leadership, are paving the way for more effective, compliant, and patient-centric healthcare data utilization.…

Data Hurdles

1
Challenging Data Management Norms: A Conversation with Malcolm Hawker, Chief Data Officer at Profisee 46:20

36 weeks之前46:20

46:20

In this insightful episode of Data Hurdles, hosts Chris Detzel and Michael Burke welcome Malcolm Hawker, Chief Data Officer at Profisee, for an in-depth discussion on the evolving landscape of data management and the role of Chief Data Officers (CDOs) in today's organizations. The conversation kicks off with Malcolm sharing his journey from product management to becoming a prominent figure in the data management space. He provides valuable insights into his experiences at Dun & Bradstreet and as a Gartner analyst, which have shaped his perspectives on data governance and strategy. A significant portion of the episode is dedicated to Malcolm's contrarian view on the data mesh architecture. He articulates why he favors the data fabric approach, challenging the underlying assumptions of data mesh and discussing the practical limitations of fully decentralized data management. This leads to a broader discussion on the importance of balancing domain autonomy with cross-functional data needs in organizations. The conversation then shifts to the impact of AI and machine learning on data governance. Malcolm shares his optimistic view on how AI could potentially solve complex data management challenges, particularly in automating governance processes and bridging the gap between structured and unstructured data. Throughout the episode, Malcolm emphasizes the need for CDOs to focus on delivering tangible value to their organizations. He criticizes the overreliance on data maturity assessments and lengthy frameworks, instead advocating for a more practical, customer-centric approach to data management. The discussion touches on the importance of quantifying the value of data initiatives and improving communication with business stakeholders. The hosts and Malcolm also explore emerging trends that CDOs should be aware of, including the integration of product management principles into data leadership roles, the growing importance of sustainability in data management, and the need to change the narrative around data quality from a burden to an opportunity. Towards the end, the conversation turns to the future of the CDO role. Malcolm expresses optimism about the long-term prospects for data leadership, while acknowledging short-term challenges. He highlights the emergence of a new generation of CDOs who are willing to question the status quo and take innovative approaches to data management. Throughout the episode, Malcolm's passion for data management and his commitment to driving change in the industry shine through. His candid insights and provocative ideas make for a compelling and thought-provoking discussion that challenges listeners to rethink traditional approaches to data leadership and governance. This Data Hurdles episode offers valuable insights for current and aspiring CDOs, data professionals, and business leaders interested in leveraging data as a strategic asset in their organizations.…

Data Hurdles

1
Stirring the Data Pot: DataKitchen's CEO, Founder, Head Chef, Christopher Bergh on Cooking Up Success 42:22

40 weeks之前42:22

42:22

This episode of Data Hurdles features an in-depth interview with Christopher Bergh, CEO and Head Chef of Data Kitchen. Hosts Chris Detzel and Michael Burke engage in a wide-ranging discussion about the challenges and opportunities in data analytics and engineering. Key Topics Covered: Introduction and Background Chris Bergh introduces Data Kitchen and explains the company name's origin and significance. He shares his background in software development and transition to data analytics. Core Challenges in Data Analytics Berg emphasizes that 70-80% of data team work is waste. He stresses the importance of focusing on eliminating waste rather than optimizing the productive 20-30%. Data Kitchen's Approach The company aims to bring ideas from agile, DevOps, and lean manufacturing to data and analytics teams. They focus on helping teams deliver insights to demanding customers consistently and innovatively. Key Problems in Data Teams Difficulty in making quick changes and assessing their impact Challenges in measuring team productivity and customer satisfaction The need for better error detection and resolution in production Data Team Productivity and Happiness Discussion on the high frustration levels among data professionals The importance of connecting data teams with end customers for better feedback and satisfaction Data Quality and Testing Bergh introduces Data Kitchen's approach to automatically generating data quality validation tests The importance of business context in creating effective tests Data Journey Concept Bergh explains the "data journey" as a fire alarm control panel for data processes The importance of having a live, actionable view of the entire data production process Observability in Data Systems Discussion on the future of observability in increasingly complex data systems The need for cross-tool and deep-dive monitoring capabilities Impact of AI and LLMs Bergh's perspective on the role of AI and Large Language Models in data work Emphasis that while AI can improve efficiency, it doesn't solve the fundamental waste problem Open Source and Community Data Kitchen's decision to open-source their software The importance of spreading ideas and fostering community in the data space Certification and Education Data Kitchen's certification program and its popularity among data professionals Key Takeaways: The most significant challenge in data analytics is addressing the 70-80% of work that is waste. Connecting data teams directly with customers can significantly improve outcomes and job satisfaction. Automatically generated data quality tests and visualizing the entire data production process are crucial innovations. While AI and new tools can improve efficiency, they don't address the core issues of waste and system-level problems in data work. Open-sourcing and community building are essential for advancing the field of data analytics and engineering.…

Data Hurdles

1
Transforming CX with AI: A Conversation with CEO and Co-Founder, Somya Kapoor of TheLoops 42:16

43 weeks之前42:16

42:16

Welcome to another episode of the Data Hurdles podcast! In this episode, hosts Chris Detzel and Michael Burke are thrilled to have a special guest, Somya Kapoor , the CEO and Co-Founder of TheLoops . Somya brings a wealth of experience from her leadership roles at SAP and ServiceNow and shares her remarkable journey of transitioning from big corporations to the startup world. Episode Highlights: Introduction and Background: Chris and Michael kick off the episode with a warm welcome and a brief catch-up before introducing Somya Kapoor. Somya shares her impressive background, highlighting her leadership roles at SAP and ServiceNow and her transition to the startup ecosystem. Founding TheLoops: Somya dives into the inspiration behind co-founding TheLoops, a company focused on transforming customer experience (CX) using AI. She recounts the challenges and opportunities she encountered while starting the company during the COVID-19 pandemic. Despite the initial setbacks, Somya's perseverance and innovative thinking led to the successful establishment of TheLoops. AI and Customer Experience: The discussion delves into how TheLoops leverages AI to enhance customer experience by aligning people, processes, and data. Somya explains the critical role of AI in operational efficiency and personalized customer interactions. She emphasizes the importance of understanding customer behavior through data and how it can drive better business outcomes. Navigating Challenges: Somya shares insights on navigating the hurdles of building a startup, especially during uncertain times. She discusses the importance of pivoting and adapting to changing circumstances, and how TheLoops managed to secure customers and investors despite the pandemic-induced challenges. Leadership and Diversity: The conversation shifts to leadership and the significance of fostering an inclusive and diverse work culture. Somya shares her personal journey of growing up in different cultural environments and how it shaped her perspective on diversity. She highlights the benefits of having a diverse team and how it contributes to creativity and innovation at TheLoops. Future Trends in CX: Somya provides her perspective on the current trends and future of the CX industry. She discusses the transformative impact of AI on CX, the breaking down of silos within organizations, and the evolving role of support leaders. Somya also touches upon the integration of AI in support systems to enhance customer satisfaction and operational efficiency. Advice for Aspiring Entrepreneurs: Towards the end of the episode, Somya offers valuable advice for aspiring entrepreneurs, especially women looking to enter the tech industry. She encourages them to take the leap, embrace challenges, and learn to navigate the startup landscape with resilience and determination. Closing Thoughts: Chris and Michael wrap up the episode with a heartfelt thank you to Somya for sharing her insights and experiences. They express their admiration for her journey and the innovative work being done at TheLoops. The hosts also remind listeners to rate, review, and subscribe to the podcast for more inspiring episodes. Follow Us: Twitter: @DataHurdles LinkedIn: Data Hurdles Website: Data Hurdles…

Data Hurdles

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AI Everywhere: The Coming Era of Intelligent Devices and Embedded Systems 23:33

46 weeks之前23:33

23:33

In this episode of Data Hurdles, hosts Chris Detzel and Michael Burke engage in a wide-ranging discussion about the current state and future trajectory of artificial intelligence (AI) and machine learning (ML) in both the job market and product development. The conversation begins with Mike sharing insights on the changing job market for AI and ML professionals. Despite the high demand for these skills in recent years, he notes that the market seems to be softening, with even experienced candidates facing challenges finding jobs. They discuss potential factors, including an oversupply of talent, ambiguity around the impact of large language models like ChatGPT, and broader economic conditions. The hosts then delve into the different challenges and opportunities facing AI startups compared to established companies looking to integrate AI into their products. Mike suggests that startups are at risk of being overtaken by the rapid advancements in foundational models like GPT-4, while larger companies have some buffer due to their existing customer base and revenue streams. However, he notes that even large organizations will need to eventually move beyond lightweight AI integrations and rebuild their products around AI foundations to stay competitive. Throughout the discussion, Chris and Mike touch on various examples of AI applications, from AI companions like Character.AI to productivity tools like Gemini's integration with Google Workspace. They also explore the importance of data privacy and security when using AI tools, highlighting how certain industries and use cases require on-premise models rather than cloud-based platforms. Looking ahead, the hosts imagine a future where AI is embedded in every device and system, from home appliances to cars. While noting the current "gimmicky phase" of many AI features, they express excitement about the potential for these technologies to eventually solve deeper, more meaningful problems. The episode offers a nuanced exploration of the challenges and opportunities surrounding AI and ML, informed by the hosts' industry experience and observations. While covering a broad range of topics, the central theme is the need for individuals and organizations to strategically navigate the rapid advancements in these technologies.…

Data Hurdles

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Pragmatic Approaches to Smart Data and AI Adoption with Founder of North Labs, Collin Graves 33:12

52 weeks之前33:12

33:12

In this episode of the Data Hurdles podcast, hosts Chris Detzel and Michael Burke interview Collin Graves, CEO and founder of North Labs, an AWS data and analytics partner based in Scottsdale, Arizona. Collin shares his background, starting with his military service and early exposure to cloud computing through Amazon Web Services (AWS) in 2007. He then discusses the founding of North Labs and its focus on helping industrial organizations, such as those in CPG, retail, and oil and gas, set data and AI strategies to drive business value. The conversation delves into North Labs' approach to smart data and AI adoption, emphasizing pragmatism and building strong foundations. Collin explains how North Labs differentiates itself by being an AWS-first company while still supporting tools like Snowflake when appropriate. Collin also shares his leadership philosophy, drawing from his military experience. He stresses the importance of struggling together, delegating effectively, and being gentle but firm. The discussion touches on maintaining customer service and excellence as a small company by being selective about projects and adhering to standard operating procedures. Looking to the future, Collin envisions North Labs as a leading non-GSI (Global System Integrator) partner for AWS customers in the data and AI space. The company aims to help organizations adopt technologies like GenAI in a measured, ROI-driven manner. Throughout the episode, Collin provides insights into navigating the evolving cloud landscape, the challenges faced by organizations of different sizes, and the importance of clear communication and strategic partnerships in driving successful data and AI initiatives.…

Data Hurdles

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The Future of AI in Product Design: Insights from Craft's Founder, Jeremy Merle 30:23

1 year之前30:23

30:23

In this episode of Data Hurdles, hosts Mike Burke and Chris Detzel interview Jeremy Merle, founder and partner at Craft, a digital product design studio. Jeremy shares his background in design and user experience, having worked with various Fortune 500 companies and startups, including his role as a founding designer at Brightcove, an online video platform. The conversation delves into Kraft's mission and vision, particularly in relation to AI. Jeremy explains how his company is investing in AI education and training for their team, as well as developing user experience principles based on their work with AI-focused products. He emphasizes the importance of creating exceptional user experiences and the need for a shared understanding of goals between Kraft and their clients. Jeremy discusses the early stages of AI integration in product design and the challenges that come with it, such as meeting users where they are in terms of their familiarity with the technology. He also touches on the potential for AI to automate certain tasks, allowing designers to focus on more strategic and conceptual work. The hosts and Jeremy explore the future of AI-powered user experiences, including personalized AI assistants that understand individual communication styles and needs. They also discuss the complexity of designing for such experiences, considering factors like security and user control. Throughout the episode, Jeremy emphasizes the importance of experimentation, challenging assumptions, and expanding one's network to stay ahead in the rapidly evolving AI landscape. The conversation also touches on the potential for startups to lead the way in AI integration, with larger companies potentially acquiring them to stay competitive. Overall, the episode provides insights into the challenges and opportunities that AI presents for digital product design, highlighting the need for designers to adapt and evolve their practices to create exceptional user experiences in an AI-driven world.…

Data Hurdles

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Unravel Data with Co-founder and CEO Kunal Agarwal: The Power of Data Observability 23:25

1 year之前23:25

23:25

In this compelling episode of the Data Hurdles podcast, hosts Chris Detzel and Michael Burke sit down with Kunal Agarwal, theCo-founder and CEO of Unravel Data , to delve into the fascinating realm of data observability. The conversation explores the challenges faced by organizations in managing complex data environments and how Unravel Data is leading the way in providing comprehensive solutions. Starting the discussion on a lighthearted note, Chris and Michael acknowledge the dedication of their guest, They express admiration for Kunal's commitment to the cause, which sets the stage for diving into the intricacies of data observability. Kunal begins by highlighting the origins of Unravel Data and its mission to simplify and optimize data pipelines. Drawing from his experience in the early days of Hadoop, he emphasizes the significance of making powerful data technologies accessible to a broader audience. By addressing issues such as security, governance, observability, and performance management, Unravel Data seeks to enhance the usability and efficiency of data environments. As the conversation progresses, Kunal and the hosts explore the evolution of data environments and the increasing need for observability. They discuss how data platforms now involve a broader range of users beyond just IT professionals, such as marketing, finance, and legal teams. Unravel Data has adapted its platform to cater to these changing dynamics, ensuring that it covers the entire data stack across different cloud platforms and services. A key aspect that sets Unravel Data apart is its effective utilization of artificial intelligence (AI) and machine learning. Kunal explains how the platform leverages algorithms and models to automatically detect issues, provide inferences, and suggest actionable insights. By presenting this information in plain language, Unravel Data empowers users, regardless of their technical expertise, to optimize their code, pipelines, and data sets. The conversation then shifts to the cultural dimension of implementing data observability. Kunal emphasizes the importance of incentivizing engineers and data professionals to proactively address inefficiencies and drive improvements. The hosts and Kunal discuss various approaches, including creating a sense of healthy competition through leaderboards or providing monetary rewards tied to cost savings. These strategies help foster a culture of continuous improvement and ownership within organizations. Looking to the future, the episode concludes with a visionary perspective on data observability. Kunal predicts that data applications will play an increasingly critical role in various industries, from transportation to banking and healthcare. With the potential impact of flawed data on human lives, the importance of observability becomes paramount. Unravel Data aims to be at the forefront, providing the insights and tools necessary to ensure smooth, reliable, and performant data operations. Listeners of this Data Hurdles podcast episode gain valuable insights into the importance of data observability and its potential to drive operational excellence. With Unravel Data at the forefront of this field, organizations can navigate the complex data landscape with confidence and optimize their data environments for long-term success.…

Data Hurdles

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Balancing Growth and Profitability: The Rule of 40 vs The Rule of X 32:56

1 year之前32:56

32:56

The main guest, Jay Nathan , shares his career journey and varied experience in startups, having founded companies, sold companies, and worked in executive roles focused on growth, customer success, and retention. Balancing growth vs profitability, explaining metrics like the "Rule of 40" that investors use to evaluate SaaS companies. He discusses how the market has changed to favor profitability more than unsustainable growth. How early stage startups should think about data, metrics, and setting up processes to enable scale. This includes tracking basic pipeline metrics, keeping data consolidated, and not over-complicating things early on. Hiring for startups - looking for "hungry, humble, and smart" people who are willing to take on varied roles and responsibilities. Cultural fit and alignment matters a lot in a small startup team. His advice for executives from large companies transitioning into startups, which includes being ready to get one's "hands dirty" with ground level work in areas like sales prospecting to deeply understand the business. There is also discussion around the exponential growth of subscription business models and how startups in this space need to understand metrics around customer cohorts, product usage, and opportunities for expansion revenue. Overall, it's an insightful insider perspective on startups, leadership, growth, and data analytics.…

Data Hurdles

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The Future of Business with Generative AI: Opportunities and Challenges 29:27

1 year之前29:27

29:27

In this conversation, Krishnan Venkata , Chief Client Officer at LatentView Analytics , discusses the impact of generative AI on various industries and business functions. He highlights the importance of understanding the business problems that can be solved with generative AI and starting with small pilots to test its effectiveness. Krishnan also addresses misconceptions about generative AI and emphasizes the need for human expertise in complex problem-solving and customer interactions. He suggests that companies should integrate generative AI into their operations by identifying use cases and creating a roadmap for implementation. Takeaways Generative AI has the potential to drive growth and solve a wide range of business problems across industries and functions. When creating decision trees with generative AI, it is important to start with unsupervised learning and continuously refine the model based on known outcomes and context. There are misconceptions about generative AI being a magic solution that can solve all problems, but it should be seen as an additional layer of intelligence that complements human expertise. Specialized agents and multi-model structures are emerging in the generative AI space, allowing for more targeted and effective communication with users. Generative AI can be particularly impactful in targeting the long tail of customers, improving self-service experiences, and personalizing customer interactions. While generative AI has its limitations, human expertise and understanding of context, sentiment, and complex relationships are still crucial in problem-solving and customer interactions. Chapters 00:00 Introduction and Personal Updates 01:23 Introduction of Krishnan Venkata and Background 02:21 Generative AI and its Impact 05:20 Creating Decision Trees with Generative AI 08:53 Misconceptions about Generative AI 11:16 Specialized Agents and Multi-Model Structure 16:22 Significant Change with Generative AI in Different Industries 18:08 Targeting the Long Tail of Customers 21:03 AI in Self-Service and Personalized Customer Interactions 25:20 The Limitations of AI and the Importance of Human Expertise 28:08 Integrating Generative AI into Operations 30:31 Closing Remarks…

Data Hurdles

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Open Sesame: How OpenAI Unlocked AI 30:17

1 year之前30:17

30:17

In this conversation, Chris Detzel and Mike Burke discuss the Rabbit R1, a phone that uses large language models to take action on behalf of the user. They explore the potential of on-device AI and its impact on app integration and simplifying complex processes. They also discuss the challenges and opportunities for AI in both B2B and B2C contexts, as well as the cost of large language models and the role of OpenAI in promoting AI to the masses. Overall, they highlight the rapid advancement of technology and the exciting possibilities for the future. Takeaways The Rabbit R1 is a phone that uses large language models to take action on behalf of the user, representing a step forward in on-device AI. The integration of services into phones and the homogenization of apps and services are trends that will simplify and streamline user experiences. AI has the potential to simplify complex processes, such as insurance policy navigation, and reduce the need for manual intervention. Reducing the cost of large language models is a challenge that needs to be addressed to make AI more accessible and scalable. The rapid advancement of technology, driven by companies like OpenAI, is transforming the way we interact with AI and shaping the future of technology. Chapters 00:00 Introduction and Personal Updates 02:08 Introduction to the Rabbit R1 03:18 The R1's Ability to Take Action 04:32 Integration of Personal Accounts 07:55 Moving AI to On-Device Technology 10:27 Integration of Services into Phones 12:58 Homogenization of Apps and Services 16:20 Simplifying Complex Processes with AI 18:00 Challenges and Opportunities for AI in B2B and B2C 20:11 Reducing the Cost of Large Language Models 23:19 OpenAI's Role in Promoting AI 25:34 The Evolution of Technology and AI 28:07 The Cost of Large Language Models 30:37 The Rapid Advancement of Technology 31:59 The Future of AI and Technology 32:09 Conclusion…

Data Hurdles

1
Data Insights: A Conversation with SD Tech's COO - Shane Mishler 33:48

1 year之前33:48

33:48

In this episode of the Data Hurdles podcast, Chris Detzel and Mike Burke interview Shane Mishler, COO of SD Tech, a managed service provider. They discuss Shane's career journey across different industries and the key skills and mindsets necessary for adapting effectively. They also explore the role of technology in small businesses, the use of data for consistency and quality, and the impact of emerging technologies like automation and AI. The conversation highlights the importance of documentation and the potential of AI in transforming business operations. Overall, the episode emphasizes the need for continuous learning and open-mindedness in the ever-evolving technology landscape. Takeaways Adapting across industries requires a mindset of continuous learning and being open to new experiences. Working in the service industry can provide valuable skills in managing clients and expectations. Technology plays a crucial role in small business growth and scalability, even in seemingly non-tech industries like food trucks and counseling. Data is essential for enhancing customer relations and service delivery, as well as making informed decisions about business operations. Emerging technologies like automation and AI have the potential to revolutionize business processes and improve efficiency. Chapters 00:00 Introduction and Holiday Plans 01:08 Introduction of Guest: Shane Mishler 02:00 Transitioning Across Industries 04:33 Key Skills and Mindsets for Adapting Across Industries 06:44 Different Paths to Success 07:43 The Value of Working in the Service Industry 09:00 Transition to SD Tech 13:23 Starting a Franchise Model 17:04 Role at SD Tech and Franchise Clients 20:16 Utilizing Data for Consistency and Quality 22:12 Using Data to Enhance Customer Relations and Service Delivery 26:29 The Role of Technology in Small Business Growth 30:03 Emerging Technologies Impacting Business Operations 35:01 Embracing Technology and Having Conversations…

Data Hurdles

1
Data, AI and the Future of Advertising - A Podcast with Awarity's CEO Aditya Varanasi 31:05

1 year之前31:05

31:05

In this episode, Aditya Varanasi , CEO and Founder of Awarity , shares insights on advertising and marketing. He discusses his background in chemical engineering and how he transitioned to marketing. Aditya explains the importance of emotion in advertising and the role of advertising in meeting consumer needs. He also discusses the future of advertising, including greater control over privacy and more relevant ads. Aditya emphasizes the need to start with a specific use case when integrating AI in advertising and the importance of being an expert in advertising tools. Overall, the conversation provides valuable insights into the world of advertising and marketing. Takeaways Emotion plays a crucial role in advertising, as it helps create a connection with consumers and influences their purchasing decisions. Advertising effectiveness is not solely determined by individual factors, but by the interaction of variables and the overall consumer experience. Targeting the right audience and delivering a compelling message are key to effective advertising. The future of advertising will involve greater control over privacy, more relevant ads, and customization based on individual preferences. Chapters 00:00 Introduction and Background 01:05 Transition to Marketing 02:24 Insights from Marketing Experience 04:17 Understanding Advertising Effectiveness 06:26 The Role of Emotion in Advertising 09:51 Defining Target Customers 11:45 Realistic Expectations for Advertising 13:42 The Future of Advertising 19:55 Customization and Personalization in Advertising 22:00 Privacy and Data Sharing 24:40 Challenges of Integrating AI in Advertising 27:16 Differentiating Among Clients 29:37 Expertise in Advertising Tools 31:10 Conclusion…

Data Hurdles

1
Regulating AI: Europe's Comprehensive AI Act 36:40

1 year之前36:40

36:40

In this episode, Chris and Mike discuss the European Union's comprehensive AI act and its impact on AI development and usage. They explore the elements of the AI act, including risk levels and exclusions, and the concerns surrounding the use of AI in various sectors. The conversation delves into the challenges of balancing ethical concerns and the legislative process. They also discuss the role of Europe in shaping global AI standards and the need for education and transparency in AI governance. Takeaways The European Union has implemented the comprehensive AI act to regulate AI development and usage, focusing on practical implementation and enforcement mechanisms. The AI act classifies AI models into risk levels and includes exclusions for military AI systems and exceptions for free and open-source AI. The legislation aims to protect individuals' rights and ensure the safe and ethical use of AI, while also considering the potential impact on society and the economy. Europe envisions its role in shaping global AI standards by setting ethical guidelines and influencing other countries to adopt similar regulations. Chapters 00:00 Introduction and Holiday Cards 00:53 Drama around Open AI 01:48 Europe's Regulations on AI 02:48 Elements of the AI Act 05:09 Risk Levels and Exclusions 06:23 Concerns about AI Impact 09:16 Exclusion of Military AI Systems 10:56 Balancing Military and Defensive AI 12:14 Key Issues in Legislative Process 13:57 Balancing Ethical Concerns 15:44 Impact of AI on Education 18:34 Challenges in AI Adoption in Education 22:23 Educating Teachers and Students on AI 23:03 EU's Role in Setting Global AI Regulations 25:24 Mixed Feelings about GDPR 30:47 Banning Biometric Systems and Face Scraping 35:42 Criteria for Large, Powerful AI Models 37:22 Europe's Vision for Shaping Global AI Standards 38:51 Conclusion…

Data Hurdles

1
OpenAI Shakeup: What Sam Altman's Ousting Means for the Future of AI 16:21

1 year之前16:21

16:21

OpenAI has been making waves in the world of artificial intelligence, but a sudden leadership shakeup has thrown the company into upheaval. In this episode, we dive deep into the drama at OpenAI, analyzing the ousting of former CEO Sam Altman and what it means for the future of the AI pioneer. We discuss how Altman was abruptly fired by OpenAI's board of directors without consulting major investors like Microsoft. In response, other leaders like Greg Brockman resigned in protest. But the story doesn't end there - just days later, Microsoft hired Altman and Brockman to lead a new AI initiative. What does this huge shakeup mean for OpenAI? We speculate on the reasons behind Altman's forced departure and the apparent power struggle going on behind the scenes. Is OpenAI shifting focus from open research to profits? Did concerns about ethics and safety play a role? With Microsoft making big moves to scoop up OpenAI's exiled leaders, what will happen to the partnership between these AI giants? Will OpenAI employees follow Altman to Microsoft? Can OpenAI recover and stay on the cutting edge of AI? What do these changes mean for the future of AI more broadly? We discuss all this drama and more - the sudden hiring of a new CEO, the future of Microsoft's AI ambitions, and which company looks poised to lead the next wave of artificial intelligence innovation. Tune in for our breakdown of the personalities, politics, and technology behind this AI power struggle.…

Data Hurdles

1
Emerging Trends in AI and ML: A Look Ahead to 2024 47:41

1 year之前47:41

47:41

The landscape of artificial intelligence and machine learning is evolving rapidly. In this podcast, hosts Chris and Michael gaze into their crystal balls to predict the top AI and ML trends that will shape the industry in 2024 and beyond. They discuss major advancements on the horizon like the evolution of large language models, proliferation of edge AI, trends in explainable AI, and integration of AI into cybersecurity. Chris and Michael also explore how AI will transform major sectors like healthcare, manufacturing, education and more. With insider knowledge and infectious enthusiasm, they analyze the breakthroughs in store for autonomous robotics, human-AI collaboration, and other under-the-radar advancements that have far-reaching implications. Whether you're an AI enthusiast or just AI-curious, tune in to learn where these extraordinary technologies are heading next. Chris and Michael combine humor, hypotheticals, and a distinctly human take on the AI revolution ahead.…

Data Hurdles

1
Data Observability: A Key Tool for CDOs to Gain Insights and Impact with Chief Product Officer, Ramon Chen 46:00

1 year之前46:00

46:00

This episode of Data Hurdles podcast features guest Ramon Chen, Chief Product Officer at Acceldata, discussing the emerging concept of data observability. Data observability involves monitoring and gaining visibility into your data supply chain to identify issues and optimize. Key Topics Covered: What is data observability? It means tracking data from raw sources through the supply chain to consumption, monitoring for reliability, quality, and performance issues. How data observability integrates with MDM systems by providing useful data profiling. It gives insights into data before it reaches MDM. The relationship between data observability and AI/ML. Good data quality is crucial for AI/ML. Data observability helps ensure quality data inputs. Real business benefits like cost savings from optimizing cloud data systems, operational efficiency, risk reduction, and improved analytics. Data observability gives CDOs the visibility they need to prove value and make an impact on data management. It is becoming essential. Predictions that data observability will see major growth and adoption over the next 3-5 years as it becomes mainstream. Key Quotes: "Data observability involves monitoring the health and reliability of data as it flows through systems in the supply chain." "It provides a 360 degree view of your data landscape." "Data observability helps CDOs prove value by equating technology investments to business impact." "It represents the biggest shift in data management that I've seen in my career."…

Data Hurdles

1
The Fast and the Furious: Altinity CEO Robert Hodges' ClickHouse Joyride 35:46

2 years之前35:46

35:46

Buckle up for a high-octane conversation on tearing up the data highways with ClickHouse. Altinity CEO Robert Hodges takes the wheel to navigate building fast analytics engines that would smoke any legacy database in a street race. Learn how their souped-up columnar database design wrings out blistering acceleration measured in milliseconds. If you crave speed, this adrenaline-filled test drive will leave you breathless. The pedal will be flat to the floor as Hodges pushes ClickHouse to the limits revealing the secrets of lightning-fast time to insight. Your analytics have never moved this fast—it’s ClickHouse or bust! The key points are: High-energy discussion on using ClickHouse for fast analytics Led by Altinity CEO Robert Hodges Explanation of ClickHouse's technical advantages that enable real-time speed Emphasis on acceleration measured in milliseconds High-adrenaline angle focusing on terms like "tear up the data highways"…

Data Hurdles

1
Is Edge Computing the Next Big Thing? 25:56

2 years之前25:56

25:56

The Evolution of Computing - From Mainframes to Mobile with Edge Computing In this episode of the Data Hurdles podcast, hosts Chris Detzel and Michael Burke have an in-depth discussion on the emerging technology of edge computing. They start by explaining what exactly edge computing is - processing data closer to the source, rather than relying solely on the cloud. Michael provides examples of edge computing use cases, like manufacturing, agriculture, and defense. Key benefits discussed include reduced costs, faster speeds, ability to operate offline, and improved data privacy and security. The hosts talk about how edge computing unlocks real-time insights for businesses and gives them a competitive edge. Michael highlights companies utilizing edge computing today. They then dive into how large language models like those from OpenAI could intersect with edge computing. This leads to implications around infrastructure needs, interactivity, and legal/policy issues as decentralized AI spreads. Overall, they predict edge computing will become the standard in the future as models shrink in size and efficiency improves. It represents the next evolution of computing, from mainframes to PCs to mobile, now putting more computing power into local devices. Listen to the full discussion and analysis on the future of edge computing technology.…

Data Hurdles

1
Role of the CDO in Leading Data Mesh and Governance Initiatives 37:05

2 years之前37:05

37:05

This episode of the Data Hurdles podcast dives into the emerging concept of data mesh architecture and the critical role of governance in implementing it successfully. Host Chris Detzel and Michael Burke interviews Lauren Maffeo, author of "Designing Data Governance from the Ground Up," about the key principles and benefits of a data mesh approach. A data mesh involves distributed domain-specific data lakes that connect to a shared catalog, enabling single access point to data while keeping it owned and managed by domain experts. Maffeo explains how this data-as-a-product model allows for more consistency, findability and quality control. Data governance and literate culture are essential, as mesh can't succeed without cross-organizational accountability, standards and incentives. The group explores obstacle of misaligned teams and importance of data literacy. Maffeo emphasizes need to showcase tangible value to business units. Burke notes potential conflicts arising from domain-specific definitions of quality. Burke highlights the CDO's role in bringing cohesion. Discussion covers data training, security, legal issues around IP rights to data used in AI systems like ChatGPT. Key takeaways include how data mesh aims to balance distributed data ownership with easy access, as well as significance of data-driven culture and CDO leadership for its success. Listen to gain valuable perspective on the data mesh trend and governance strategies to enable the democratization of organizational data.…

Data Hurdles

1
AI Regulation: Striking a Balance Between Innovation and Ethics 32:41

2 years之前32:41

32:41

The podcast is hosted by Chris Detzel and Michael Burke. They discuss regulating AI development and ethical issues around emerging technologies like large language models. Governments have historically been slow to regulate new technologies. AI development is advancing rapidly, so regulators need to be more proactive. Striking a balance between fostering innovation and ensuring responsible/ethical AI is tricky. Governments could provide incentives for ethical practices or collaborate with developers on guidelines. Some examples of AI regulations so far include GDPR in the EU. China also has strict AI guidelines. Regulations can sometimes overreach and stifle innovation though. Public input and international cooperation will be important for aligning regulations with societal values and needs. Companies using AI now like chatGPT have a competitive advantage, but this window may close as the tech becomes more widespread. Overall the podcast covers the challenges around regulating AI as the technology quickly evolves, and how governments, companies, and the public can work together to promote ethical and responsible AI innovation.…

Data Hurdles

1
Data Storytelling with Scott Taylor the Data Whisperer 35:41

2 years之前35:41

35:41

The podcast episode of Data Hurtles, featuring hosts Chris Detzel and Michael Burke, along with guest Scott Taylor, known as the "Data Whisperer". We discuss challenges with data management and master data management (MDM) at large enterprises. Common issues include duplicate customer records, inconsistent hierarchies, lack of data governance and standards, and poor data quality overall. Scott talks about how to "sell" the value of MDM and data management to executives and stakeholders by focusing on business problems it can solve rather than technical jargon. He gives examples of framing it around enabling the company's strategic goals. They discuss reasons why data quality and MDM are often overlooked, despite their importance. Factors include it not being "sexy" or exciting work, politics within organizations, and constantly changing data buzzwords/hype cycles. Scott emphasizes first principles - no matter what new technologies arise, you still need a solid data foundation. He boils it down to getting four things right: unique codes, common hierarchies, a taxonomy, and consistent geographies. They give tips for CDOs and data leaders on gaining internal traction, like tying data to business problems, building a case study, and maintaining both a strategic vision and tactical focus. Overall the discussion focuses on storytelling, fundamentals, and framing data management in business terms rather than technical details. Scott provides colorful examples and stresses the need for strong data foundations.…

Data Hurdles

1
Designing Effective Data Governance: Insights and Strategies from Lauren Maffeo 25:51

2 years之前25:51