The AI Data Fabric Show

Promethium

5.0 (5)
TECHNOLOGY
UPDATED BIWEEKLY

Welcome to The AI Data Fabric Show where we explore what it means to create a modern data experience for everyone in an organization, from data analysts to non-technical business users. Each episode features interviews with data leaders, practitioners, and experts who share their insights and strategies for designing and delivering exceptional data experiences that drive business value.

FEB 3

EP 12 — How blending cell phone data with distribution systems drove 4% sales growth without changing products

Kjersten Margaret Moody's 4% sales uplift in Southeast Asia came from a data combination most CDOs overlook: cell phone movement patterns merged with internal distribution data. The insight was brutally simple yet invisible until the data revealed it. Convenience stores next to elementary schools were being restocked for night shift workers instead of parents picking up kindergarteners. Kjersten Moody, now CEO of North America at Elai and former CDO at Unilever, State Farm, and Prudential, walks through the mechanics of this win and why it represents a broader problem: most business strategies don't actually require AI to succeed, which means your data initiatives lack the forcing function of incentive alignment. Her governance framework from State Farm cuts through the bureaucracy paralysis: cars have brakes so they can go fast, not slow down. Topics discussed: Merging cell phone movement data with internal distribution systems to identify restocking mismatches by location type Structuring data office charters with explicit "what we don't do" boundaries to prevent scope creep and mission drift Aligning AI initiatives to business strategy through incentive structures rather than relying on voluntary adoption Building cross-functional teams that balance institutional knowledge with external thinking while screening for collaboration over brilliance Establishing shared KPIs with CFOs and P&L owners instead of data office vanity metrics Implementing governance frameworks that define clear operational boundaries to enable faster deployment cycles Replacing passive-aggressive meeting culture where verbal agreement doesn't translate into resource commitment or execution Accelerating business domain expertise by attending customer industry conferences instead of data and analytics technical events

38 min
12/10/2025

EP 11 — Generac's Neil Bhandar On Speaking Data Fluently Across Marketing, HR & Supply Chain

At Generac, Neil Bhandar expans his 20+ year career into IT. Previously at P&G, JPMorgan, and other companies, he operated within business functions. He's a business executive who works in data, not the other way around. This explains how he sees the same sawtooth pattern in inventory replenishment and credit card balances, and launched Tide Coldwater to 4% market share gains in under four weeks. At P&G during the second Bush administration, oil jumped from $45 to $105 per barrel. His team positioned an enzyme-activated detergent as consumer cost savings, combined it with down-counting to convert Tide from a retail loss leader into a profitable product, then targeted only retailers with high category close rates and golden households. Four weeks later: 4% market share gain for a $7 billion brand. At JPMorgan, he used organizational network analysis to find which business units weren't communicating. When he tested whether "happy employees make happy customers," he found no data that validated it. His book The Cost of Curiosity addresses analytics' broken economics where each follow-up question costs as much as the first. His biggest AI concern isn't hallucination but the sea of sameness when competitors use identical foundation models. Topics discussed: Three-pronged retail strategy delivering 4% market share gains in four weeks for $7 billion brand Down-counting technique converting retail loss leader products into profitable SKUs with built-in retailer margin Targeting golden households doing multiple loads per day through high close-rate retail channels Applying sawtooth geometric patterns across inventory optimization and credit card balance modeling domains Using organizational network analysis to surface invisible communication gaps at 250,000-person institutional scale Testing whether "happy employees make happy customers" assumption holds across full employee survey dataset Moving between supply chain, marketing, financial services, and HR by modulating technical accent per audience Sea of sameness risk when competitors deploy identical pre-trained foundation models without differentiation Resonance effects from multiple autonomous AI agents built on same models making independent decisions Reducing marginal cost of answering analytical questions to approach zero through Alexa-like interaction model

31 min
10/16/2025

EP 10 — Rubrik's Ajay Sabhlok on Embedding Architecture as The IT Decision Layer

Ajay Sabhlok made architecture the gatekeeper for every IT decision at Rubrik, not a review function that validates choices after they're made. This structural shift meant architects evaluated tools, processes, and vendor capabilities before budget conversations happened. The outcome: scaling from 700 employees to IPO without the shadow IT proliferation or rationalization projects that typically emerge when business units control procurement. The execution reveals how it works. At VMware, he performance tested Salesforce Service Cloud despite enterprise customers not doing that, found telecom provider bottlenecks, and co-developed DR capabilities with their product team. At Rubrik, every enterprise tool passes through a quarterly board with the CFO, CRO, and co-founder before purchase approval. When sales requested territory management software, the CRO delayed it three years until manual processes actually broke at scale. His "comprehensive thinking" framework maps failure scenarios during discovery, not after implementation. It's why Rubrik declared cloud-only infrastructure early and centralized all software budget authority under IT. Topics discussed: Embedding architecture as gatekeeper versus post-decision review function Quarterly C-suite Project Review Boards that control enterprise tool procurement Performance testing SaaS vendors at enterprise scale despite vendor resistance Co-developing missing product capabilities with vendor engineering teams Timing tool purchases to operational breaking points versus business requests Comprehensive thinking framework for mapping failure modes in discovery Declaring cloud-only infrastructure before hitting organizational scale Centralizing software budget authority to eliminate shadow IT formation

35 min
08/08/2025

EP 9 — Fidelity’s Mihir Shah On The Data Revolution: 3 Decades of Architecture Lessons

How do you convince executives to fund 14-month foundational builds when CDOs typically have quarters to prove value? After orchestrating multiple enterprise transformations at Fidelity—including bringing all enterprise data into a single cataloged, modeled platform—Mihir Shah reveals the tactical playbook that enabled 30 years of technology migrations without losing architectural coherence. Shah's approach stems from hard-won lessons at Churchill Insurance in the late 80s, where his team built real-time underwriting systems that processed "billions and billions of rows" while customers waited on calls. That foundational discipline—investing 14 months in architecture before launching—became his template for navigating technology shifts from mainframes to cloud-native AI platforms. His counterintuitive strategy: aggregate all planned data initiatives across business units to reveal the true cost of fragmented execution, then redirect that same budget toward unified foundations. The result? Technology-portable architectures that survive decades of vendor transitions while delivering immediate business value. Topics discussed: Shah's enterprise aggregation framework: Map all planned data warehouses, hardware refresh cycles, and modernization costs across business units over five years—this identical budget funds unified architecture while eliminating the compromise of delayed use case delivery. Breaking the CDO death spiral of vertical use case execution that creates technical debt: Aggregate requirements at enterprise or business unit level first, then execute against a unified blueprint rather than chasing individual departmental priorities. The Churchill discipline applied to enterprise scale: 14-month foundation builds that process "billions and billions of rows" in real-time—translating to modern architectures that handle enterprise query loads while maintaining sub-second response times. Technology portability strategy for stateful data layers: Accept vendor lock-in as inevitable for databases while maintaining CSP independence (Azure, GCP, AWS, Oracle) through deliberate technology selection across the entire analytics stack. Data model invariance as competitive moat: While business processes change quarterly with management shifts, properly designed business data models remain stable across decades—making them the core IP worth protecting through technology transitions. Structured data renaissance beyond the LLM hype: Combining first-party transactional intelligence (general ledger, trades, sales) with unstructured processing rather than abandoning traditional analytics for generative AI completely. Self-service at enterprise scale: Enabling power users within departments to create curated views for finance, risk, compliance, and security functions rather than expecting end users to navigate 5,000-table analytics warehouses directly. Role-specific data independence: Eliminating cross-departmental data dependencies—project managers access project status without finance teams, finance reviews project health without project managers—through persona-driven data design. Listen to more episodes: Apple Spotify YouTube Website

27 min
04/01/2025

EP 8 — PuppyGraph’s Weimo Liu on Unlocking Graph Database Value Without Data Migration

Ever wondered why graph databases promise so much but often fail to reach production? In this episode of The Data Fabric Show, Kaycee talks with Weimo Liu, CEO of PuppyGraph, about creating the "Trino of graph databases." Drawing from his experience at a previous graph startup and Google's F1 team, Weimo describes how PuppyGraph enables organizations to visualize complex relationships like money laundering patterns or supply chain dependencies without moving data from existing platforms like Snowflake, Oracle, or Databricks. By treating data logically as a graph while physically remaining as tables, PuppyGraph has broken through the adoption barrier that has plagued graph database implementations for years. Topics discussed: How PuppyGraph enables organizations to leverage graph capabilities without moving data from existing sources like Oracle, Snowflake, or Databricks. The critical insight that, despite strong interest and clear use cases, many graph database projects fail to reach production due to ETL complexity and data migration costs. Why financial fraud detection benefits from graph visualization when tracing money through multiple fake accounts, offering clear relationship mapping that SQL queries can't match. The strategic decision to partner rather than compete with established graph database vendors like Neo4J by developing complementary technology that expands the overall market. How graph relationships provide enhanced semantic context for large language models compared to raw SQL tables, improving AI understanding of complex data relationships. The development of multi-stage analytics pipelines where SQL processes initial data, graph visualizes relationships, and results feedback to data lakes for machine learning. Why modern data lake architectures enable organizations to solve complex problems using specialized tools for each phase, eliminating the vendor lock-in that previously made integrated workflows prohibitively expensive. Check out Puppygraph on LinkedIn, YouTube and on X!

34 min
02/27/2025

EP 7 — MTSI’s Joyce L. Myers on The Emotional Dimension of Data for Technical Teams

Joyce L. Myers, CDO of MTSI, considers herself more of a "builder" digital officer who's architecting additions to an already successful foundation. In this episode of The Data Fabric Show, Joyce tells Kaycee her approach to building upon the strong foundation of an employee-owned defense contractor with a 31-year history. She explains how MTSI recognized the need for better data organization as they continued to grow, comparing their situation to an entrepreneur with too many files in cabinets who can't find what they need. Joyce discusses the unique challenges of implementing data governance and AI solutions within the constraints of the public sector and defense industry, where security and protection of proprietary, sensitive, and classified information are paramount concerns. This security context creates additional complexity when adopting GenAI tools, requiring careful consideration before implementing solutions that might expose sensitive data. Topics discussed: The architectural challenges of integrating data governance as the first CDO in a 31-year-old employee-owned defense contractor, requiring a "builder" mentality to preserve institutional knowledge while implementing modern practices. How defense-specific security constraints create unique implementation pathways for GenAI, requiring careful validation processes and air-gapped environments that balance innovation against protection of classified information. The implementation of a trust-driven data quality framework that transforms "garbage in, garbage out" to "goodness in, goodness out." Strategies for enabling secure self-service analytics within highly regulated environments, emphasizing documented iterative processes that capture decision context for future governance and compliance requirements. The evolution from tightly-controlled ETL processes toward a more agile data fabric approach that creates a sandbox environment for experimentation before formalizing pipelines. The comprehensive metadata context needed to create effective data products in defense applications — capturing not just data lineage but decision authority, classification levels, and intended analytical purpose. Applying NASA's mission-focused organizational alignment to defense data teams, where every contributor understands how their component supports operational objectives regardless of technical specialty. The development of an iterative data discovery workflow for security-conscious environments that preserves context while enabling business users to safely explore and refine analytical questions.

27 min
01/07/2025

EP 6 — Penn State’s Bruce Desmarais on Social Data Analytics and Combating Misinformation

In this episode of The Data Fabric Show, Kaycee speaks with Bruce Desmarais, PhD, Professor & Director of the Center for Social Data Analytics, Penn State University. Together, they dive into the role of advanced data science tools in analyzing social media data to address issues like misinformation and online bullying. Bruce shares insights on the collaboration between academia and tech companies. The conversation also explores the transformative potential of generative AI in social research, including the creation of synthetic social systems for experimentation. Topics discussed: The significance of social data analytics in understanding human behavior and communication through digital traces left on social media. Challenges researchers face in accessing and integrating diverse data sources, especially with changing data formats and proprietary data restrictions from social media companies. The importance of collaboration between academia and industry in addressing real-world problems, with examples of successful partnerships that enhance data analytics efforts. The role of generative AI in streamlining data analysis processes, including its potential to automate data tagging and quality checks in research. Ethical considerations in social research, particularly when experimenting with interventions on social media platforms and the implications for freedom of speech. The impact of generative AI on survey design and data collection, allowing researchers to generate realistic synthetic responses for more efficient analysis. The need for data validation and quality checking in research, emphasizing the importance of unit testing to ensure accurate results from AI-generated outputs. The future of social data analytics and the potential for creating synthetic social systems to explore complex interactions without ethical concerns or real-world consequences. The ongoing challenges of data governance and security in enterprise analytics, highlighting why many organizations are hesitant to fully adopt generative AI technologies.

27 min
12/02/2024

EP 5 — Databricks’ Robin Sutara on Transforming Data Culture in Organizations

In this episode of The Data Fabric Show, Kaycee speaks with Robin Sutara, Field Chief Data Strategy Officer at Databricks, who shares her unique journey from repairing Apache helicopters in the U.S. Army to leading data strategies in the tech industry. Robin dives into the importance of fostering a data-driven culture within organizations and the critical role of change management in digital transformation. Additionally, Robin discusses how generative AI can enhance data strategies, emphasizing the need for robust data foundations to ensure trust and accuracy in AI outputs. Topics discussed: The significance of fostering a data-driven culture within organizations is crucial for successful digital transformation and maximizing the value of data initiatives. How change management is essential for ensuring that new technologies are embraced by business users, bridging the gap between tech and business needs. How generative AI is revolutionizing data strategies by automating processes, enhancing data quality, and enabling organizations to make more informed decisions. The importance of data governance and transparency in building trust around AI outputs, ensuring that users can rely on the data provided. Why organizations must focus on creating simple, consistent user interfaces that allow business users to engage with data without needing extensive technical knowledge. The need for collaboration between technical teams and business users to align data initiatives with organizational goals and drive meaningful outcomes. The necessity of investing in data foundations to support generative AI and ensure the accuracy and reliability of insights generated. The evolving landscape of AI and data technologies requires organizations to remain adaptable and open to integrating new solutions into their existing ecosystems.

43 min
10/17/2024

EP 4 — National Grid Electrical Transmission’s Matt Clark on Navigating the Evolving Role of Chief Data Officers

In this episode of The Data Fabric Show, Kaycee speaks with Matt Clark, Data Leader of National Grid Electrical Transmission, who shares his expertise on the evolving landscape of data leadership. He discusses the various roles of CDOs, including the caretaker, arsonist, phoenix, and scapegoat, highlighting the unique challenges and opportunities each role presents. Matthew emphasizes the importance of empowering business users to take ownership of data and analytics, fostering a culture of collaboration and innovation. He also explores how emerging technologies like data fabric and GenAI can enhance data accessibility and quality, paving the way for more effective decision-making in organizations. Topics discussed: The evolving role of chief data officers, including caretaker, phoenix, arsonist, and scapegoat, and how these roles impact organizational effectiveness and culture. The importance of understanding organizational culture when stepping into a data leadership role and how it influences the success of data initiatives. Strategies for empowering business users to take ownership of data, fostering a culture of accountability and collaboration within organizations. The challenges data leaders face when trying to drive transformation and innovation in organizations resistant to change and traditional practices. The role of emerging technologies, such as data fabric and GenAI, in enhancing data accessibility, quality, and overall decision-making processes. The significance of aligning data strategies with business objectives to ensure that data initiatives deliver tangible value to the organization. The necessity of building strong relationships with stakeholders to gain buy in for data initiatives and ensure successful implementation. The impact of rapid technological advancements on data workflows and the need for data leaders to adapt to these changes effectively.

54 min
09/25/2024

EP 3 — Night Market’s Dr. Ram Singh on Driving Innovation through Rapid Prototyping

In this episode of The Data Fabric Show, Kaycee speaks with Dr. Ram Singh, Chief Data Officer at Night Market, who shares his expertise on the intersection of data analytics and marketing. He emphasizes the significance of composable analytics in driving tangible business outcomes and enhancing customer experiences. Dr. Singh discusses the importance of rapid prototyping, which fosters innovation and collaboration within organizations, allowing teams to uncover unexpected use cases. Additionally, he explores the evolving role of AI in marketing, highlighting its potential to optimize processes and improve customer engagement. Dr. Singh offers valuable insights on aligning analytics with business goals and leveraging data for success in today’s competitive landscape. Topics discussed: How composable analytics enables businesses to integrate data sets effectively, leading to improved decision-making and enhanced customer experiences. The importance of demonstrating measurable business results from analytics initiatives is emphasized, ensuring that data-driven strategies align with organizational goals. How rapid prototyping encourages innovation and collaboration, allowing teams to quickly test ideas and adapt to changing business conditions. The evolving role of artificial intelligence in marketing is explored, focusing on its ability to optimize processes and drive customer engagement. The necessity of leveraging data to inform strategic decisions, ultimately leading to better business performance and competitive advantage. The importance of cross-functional collaboration in data projects, fostering a culture of shared insights and collective problem-solving. Strategies for extracting value from data in smaller increments, allowing businesses to see results without lengthy implementation timelines. The need for analytics initiatives to be closely aligned with overarching business objectives to ensure relevance and impact. The significance of maintaining a customer-centric focus in analytics, ensuring that insights directly enhance customer experiences and satisfaction. The common challenges organizations face in integrating various data sources, offering insights on overcoming these obstacles for better analytics outcomes.

39 min
09/17/2024

EP 2 — Acceldata’s Ramon Chen on Enhancing Data Quality Through Active Metadata

In the second episode of The Data Fabric Show, Kaycee Lai, Founder of Promethium, and Ramon Chen, CPO at Acceldata, discuss the dynamic world of data observability as a critical function for ensuring data quality, cost efficiency, and operational effectiveness. They explore the emerging relevance of active metadata, which enables organizations to leverage AI for building smarter data products and managing data more effectively, as well as the challenges and benefits of integrating data observability with active metadata and how it creates a more comprehensive view of data lineage and quality, enhancing trust in data for AI applications. Ramon also shares his insights on modernization strategies, such as migrating data from legacy systems like Hadoop to the cloud, while using data observability to avoid moving redundant or irrelevant data, and critical need for visibility across the data pipeline to prevent inefficiencies and ensure a clear understanding of the data landscape. Throughout the episode, they emphasize the importance of balancing technology and business goals, understanding market needs, and fostering collaboration between business and IT to achieve a modern, efficient, and transparent data experience. Listen in for practical insights on creating a data-driven strategy, leveraging AI, and building a modern data observability framework that aligns with your organization's objectives. Topics discussed: The importance of data observability in identifying and resolving data quality issues before they escalate into costly problems. The "shift left" approach, which advocates addressing data quality at the point of entry to enhance overall efficiency. How active metadata can provide essential context, improving trust and understanding of data across organizations. The transformative role of generative AI in data management, enabling better insights and decision-making capabilities. The need for collaboration between data teams and business users to ensure data-driven decisions are informed and effective. The necessity of having end-to-end visibility in data pipelines to understand dependencies and potential impacts on business operations. The evolving role of chief data officers and the challenges they face in maintaining data governance and quality. The effectiveness and efficiency of various tools used for data quality management and their associated costs.

26 min
09/05/2024

EP 1 — BigID’s Peggy Tsai on Building Trust as a Chief Data Officer

In this episode of The Data Fabric Show, our host, Kaycee Lai, Founder of Promethium, speaks with Peggy Tsai, Chief Data Officer at BigID. Peggy shares her expertise on the evolving role of data governance in today’s organizations. She discusses the importance of leveraging AI technologies to enhance data initiatives and the critical need for effective communication with stakeholders. Peggy also emphasizes how CDOs can navigate challenges by focusing on building trust and collaboration within their teams. Additionally, she highlights the significance of storytelling in articulating the value of data-driven strategies. Peggy offers valuable insights into the strategies that can help CDOs succeed in an increasingly complex data landscape! Topics discussed: How the responsibilities of Chief Data Officers are changing in response to technological advancements and organizational needs. Common challenges CDOs face in implementing effective data governance frameworks within their organizations. The importance of integrating AI into data strategies to enhance efficiency and drive better decision-making processes. The need for CDOs to establish trust and collaboration with stakeholders is crucial for successful data initiatives and project buy-in. The importance of clear communication in articulating data solutions and addressing the needs of various stakeholders. How achieving quick wins can help CDOs build credibility and demonstrate the value of data initiatives to leadership. The significance of storytelling in conveying the impact of data projects and making complex data concepts accessible to all. How a deep understanding of business objectives is essential for CDOs to align data strategies with organizational goals and drive value. The importance of investing in employee education and upskilling to adapt to evolving data landscapes. Strategies for CDOs to manage relationships with vendors and ensure they align with the organization's data strategy and goals.

32 min

12 Episodes

out of 5

5 Ratings

Creator

Promethium
Years Active

2024 - 2026
Episodes

12
Rating

Clean
Show Website

The AI Data Fabric Show

The AI Data Fabric Show

Episodes

EP 12 — How blending cell phone data with distribution systems drove 4% sales growth without changing products

EP 11 — Generac's Neil Bhandar On Speaking Data Fluently Across Marketing, HR & Supply Chain

EP 10 — Rubrik's Ajay Sabhlok on Embedding Architecture as The IT Decision Layer

EP 9 — Fidelity’s Mihir Shah On The Data Revolution: 3 Decades of Architecture Lessons

EP 8 — PuppyGraph’s Weimo Liu on Unlocking Graph Database Value Without Data Migration

EP 7 — MTSI’s Joyce L. Myers on The Emotional Dimension of Data for Technical Teams

EP 6 — Penn State’s Bruce Desmarais on Social Data Analytics and Combating Misinformation

EP 5 — Databricks’ Robin Sutara on Transforming Data Culture in Organizations

EP 4 — National Grid Electrical Transmission’s Matt Clark on Navigating the Evolving Role of Chief Data Officers

EP 3 — Night Market’s Dr. Ram Singh on Driving Innovation through Rapid Prototyping

EP 2 — Acceldata’s Ramon Chen on Enhancing Data Quality Through Active Metadata

EP 1 — BigID’s Peggy Tsai on Building Trust as a Chief Data Officer

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