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

Data Hurdles

MP3•Pagina principale dell'episodio

28:30

We're trying something different this week: a full post-show breakdown of every episode in the latest season of Black Mirror! Ari Romero is joined by Tudum's Black Mirror expert, Keisha Hatchett, to give you all the nuance, the insider commentary, and the details you might have missed in this incredible new season. Plus commentary from creator & showrunner Charlie Brooker! SPOILER ALERT: We're talking about the new season in detail and revealing key plot points. If you haven't watched yet, and you don't want to know what happens, turn back now! You can watch all seven seasons of Black Mirror now in your personalized virtual theater . Follow Netflix Podcasts and read more about Black Mirror on Tudum.com .…

circa un anno fa 50:58

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

51 episodi

Data Hurdles

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

Data Hurdles

published circa un anno fa

MP3•Pagina principale dell'episodio

With over a decade of experience in FinTech and now leading data initiatives at dbt Labs, Alex shares valuable perspectives on:

• 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 episode concludes with valuable insights about preparing organizations for emerging AI trends and the importance of considering security implications in an AI-enabled future.

51 episodi

Tutti gli episodi

Data Hurdles

1
Vital Industries Transformed: Inside Fusable's Data Strategy with Chief Data Officer, Matthew Cox 37:42

3 giorni fa37:42

37:42

In this revealing episode of "Data Hurdles," hosts Chris Detzel and Michael Burke interview Matthew Cox, Chief Data Officer at Fusable, about his journey transforming data strategies across traditionally underserved industries. Matthew shares his unique position overseeing product, data, engineering, cybersecurity, enterprise applications, and professional services at Fusable - a company created from multiple acquisitions to deliver vital data services to agriculture, construction, and trucking industries. The conversation explores how these essential sectors, often overlooked in data innovation, are being revolutionized through connected data strategies. Listeners will gain insights into Matthew's vision for building customer trust through data quality, his excitement about agentic AI's practical applications, and how Fusable creates value by meeting customers at their "moment of truth" when decisions are made. The episode highlights the progression from data-driven to insight-driven decision making and reveals how Matthew's experience at Google informs his approach to democratizing advanced data capabilities across industries that form the backbone of our economy. A must-listen for data leaders looking to connect traditional business models with cutting-edge data strategies and AI applications.…

Data Hurdles

1
Enterprise Data Observability and the Future of Agentic AI with Ramon Chen, Chief Product Officer at Acceldata 30:14

10 giorni fa30:14

30:14

In this thought-provoking episode of Data Hurdles, hosts Chris Detzel and Michael Burke welcome back Ramon Chen, Chief Product Officer at Acceldata, for an insightful discussion on the rapidly evolving world of enterprise data observability and agentic AI. Ramon shares how data observability has evolved from an emerging concept to a "full-blown tidal wave" in the industry, now widely recognized as a crucial component of data management that ensures proactive data quality and trustworthiness throughout the data supply chain. The conversation explores how data observability functions as a set of policies and rules that monitor data quality from inception, providing data engineers with timely alerts to resolve issues before they affect business users' reports or downstream AI applications. The episode dives deep into Acceldata's recent announcement of "Agentic AI data management" - a paradigm shift that applies AI agents to data management in a way similar to their application in customer support and sales. Ramon explains how this approach offers a chat-like interface that adapts to the user's role and intent, providing personalized insights and recommendations about data quality and reliability. The hosts and Ramon also discuss broader implications of AI advancement, including the changing nature of technical roles, the balance between automation and human oversight, and the emergence of AI observability as a natural extension of data observability. Ramon highlights the upcoming "Autonomous 25" conference on May 20th in San Francisco, where industry leaders will explore agentic AI and its impact on data management. This episode offers valuable insights for data professionals navigating the intersection of AI and data management in an era of unprecedented technological change.…

Data Hurdles

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

17 giorni fa40:13

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

4 weeks fa43:36

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

8 weeks fa24:42

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

8 weeks fa33:46

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

20 weeks fa1:07:23

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

1
The Future of Data Teams in the AI Era: Insights from Alex Welch, dbt Labs' Head of Data and Analytics 50:58

24 weeks fa50:58

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

1
Data Mesh in Action: Challenges, Opportunities, and Real-World Examples with Willem Koenders 42:27

29 weeks fa42:27

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

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

36 weeks fa27:20

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

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

38 weeks fa46:20

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

42 weeks fa42:22

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

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

45 weeks fa42:16

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

1
AI Everywhere: The Coming Era of Intelligent Devices and Embedded Systems 23:33

48 weeks fa23:33

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

1 year fa33:12