unDUBBED

Summary In this episode, Steve Holley, a Tableau Solutions Engineer at Salesforce, shares insights on the evolving landscape of headless analytics and AI, the importance of trust and governance in data, and how organizations can adapt to rapid technological change. Keywords Data Analytics, Headless AI, Tableau, Salesforce, Data Governance, AI in Business, Data Transformation, Analytics Tools, Data Strategy key topics Headless analytics and AI Data governance and trust Real-time data insights and alerts Semantic models and data sources Organizational change management in data guest name Steve Holley Titles The Future of Headless Analytics and AI in Business How Salesforce is Shaping Headless Data Solutions Chapters 00:00 Introduction to Undubbed Podcast 02:00 Meet Steve Holley: Tableau Solutions Engineer 03:11 The Impact of AI on Data Interaction 03:28 Understanding Headless Analytics and AI 06:54 The Role of Dashboards in Data Analysis 09:56 Trust and Accuracy in AI Insights 15:16 Live Demo: Data Interaction in Slack 19:51 Understanding Dashboard Insights 21:41 Identifying At-Risk Accounts 23:33 Defining Business Rules and Context 25:49 The Role of Data Professionals 30:10 Adapting to New Data Workflows 33:31 Change Management in Data Analysis 35:31 Taking Action on Insights 41:11 Learning from Others in the Community resources Salesforce Links Official Website - https://www.salesforce.com/ Tableau Official Website - https://www.tableau.com/ Y, W A I I - Knowledge Mapping Tool - https://www.ywaii.com/ Steve's LinkedIn - https://www.linkedin.com/in/steveholley/ Steve's Twitter - https://twitter.com/steveholley 

Episode Summary Dive into the evolving world of data, AI, and analytics with Will Pitzler, Director of Product Management at Tableau. This episode explores how Tableau is breaking down barriers to make insights accessible across platforms, the importance of governance, and what it actually takes to become a data-driven organisation.   In this episode, you'll learn about: ● Why leading LLMs scored just 6% on a Yale benchmark testing real enterprise databases and what that means for your AI strategy ● The role of composable data sources, the most requested feature in Tableau's history, and what they unlock for data teams ● How Tableau insights are now accessible directly inside Google Sheets, PowerPoint, Google Slides, and Word ● The uncomfortable questions around PII, data sovereignty, and governance when using MCP and third-party AI tools ● The Open Semantic Interchange initiative, what it is, why it matters, and how far away a real standard actually is ● What data leaders should actually do before their next AI project kicks off Timestamps: 00:00 - Introduction and welcome 01:22 - Will's background and role at Tableau 03:31 - Why TC26 felt different, the return to the practitioner 06:00 - The developer spectrum and Tableau's broad user base 06:30 - Tableau through the Salesforce acquisition, what's changed and what hasn't 09:42 - TC26 comes to Sydney, bringing the insights to local customers 10:32 - The Yale Spider 2.0 benchmark and the 6% problem 13:50 - Why context is everything for LLMs in enterprise environments 15:18 - PII, data sovereignty, and the governance gap in AI and MCP 18:08 - What data leaders are actually telling Will on the ground 19:39 - Open Semantic Interchange, the industry's attempt at a common standard 21:17 - Two schools of thought on how Tableau handles semantic layers 23:23 - Delegated semantics, Tableau's interim approach 26:06 - How the data market has gone in circles, monolithic to modern and back again 28:30 - Composable data sources, the most requested feature in Tableau's history 33:10 - Governance and ways of working as teams move faster 35:25 - The last mile problem, insights shouldn't live only inside Tableau 38:44 - Staying in the flow, self-service where people actually work 39:32 - The tension between AI text outputs and data visualisation 42:02 - Demo begins, third party integrations overview 44:38 - Demo: Tableau inside PowerPoint 46:09 - The timestamp feature and refreshing slides on demand 47:19 - Salesforce internal use case, automating operational reporting 50:40 - Why live dashboards weren't the answer 52:06 - Demo: Tableau inside Google Sheets 55:27 - Using published data sources in Google Sheets 56:58 - Licensing and permissions 57:22 - Pushing data back into Tableau from Google Sheets 59:48 - The Henry Ford problem, what customers say vs. what they need 1:02:28 - Closing question: What should a data leader actually do? 1:03:16 - Where to find Will 1:03:38 - Fi and Sarah's closing takeaways   Resources & Links: Connect with Will on LinkedIn https://www.linkedin.com/in/will-pitzler-603b915b/ Spider 2.0 Benchmark, Yale https://spider2-sql.github.io/ Tableau Add-on for Google Workspace https://www.tableau.com/blog/improve-collaboration-tableau-google-workspace Tableau App for Microsoft 365 https://www.tableau.com/blog/meet-tableau-app-for-microsoft-365-word-powerpoint-teams  

Summary Fresh from Tableau Conference 2026, Fi and Sarah sit down with Kirk and Candi Munroe - co-founders of Paint with Data, a Tableau consultancy and Salesforce partner based in Canada - to unpack what's actually coming for Tableau and what it means for the people making decisions about their data teams. Kirk brings 25 years in business analytics, a newly minted Tableau Visionary title, as well as a Tableau Ambassador and a book on data modelling in Tableau. Candi is a Visual Analytics specialist, four-time Tableau User Group Ambassador, and heads up the Canada Tableau User Group. Between them, they've seen every era of Tableau - and they're genuinely excited about this one. The conversation covers Tableau Solve and its write-back and forecasting potential, the Viz layers that will let you layer multiple data sources on a single chart axis, composable data sources and why they're the missing piece in Tableau's semantic layer story, agentic analytics and what it actually takes to make natural language querying work in practice, Tableau MCP - including a cautionary tale about a CPM field that cost 58 cents a query - and what the Devs on Stage roadmap means for the data leaders who need to decide what comes next for their teams. Takeaways Tableau Solve productionises record-level triage and forecasting - and write-back is finally coming, with Kirk calling it "way, way, way overdue" Chart Layers will allow multiple data sources to sit on the same XY axis without any joining, opening up time series comparisons and complex formatting that previously required workarounds Tableau Server is getting genuine love - including LLM features - signalling that Tableau sees it as a long-term platform, not a migration waiting to happen Composable data sources let analysts extend a published data source on the fly without touching the original, and because everything is relationship-based, nothing already built gets broken Well-governed, well-commented data sources are now a prerequisite for agentic analytics - without them, the AI will guess wrong and cost you money (Kirk's CPM example is a must-hear) Tableau MCP is already live, easy to set up in Claude Desktop in under three minutes, and keeps row-level security intact - but you need your own personal access token, not a service account AI-assisted field descriptions get you 90-95% of the way there - the blank box problem is real, and the AI solves it The Tableau Desktop Authoring API is in its third release, and style guide automation - padding, corner rounding, colour palette - is close Headless BI and Slack-first data experiences are coming, and they matter most for the people who will never naturally log into Tableau Data leaders should position themselves as connective glue across departments - spotting correlations between silos that no single team can see Chapters 00:00 Introduction to Tableau Conference 2026 Highlights 01:52 Meet the Experts: Kirk and Candi 03:32 Exciting Announcements from TC26 05:40 Tableau Solve: Enhancements and Features 07:52 The Importance of Write-Back Functionality 09:31 Map and Chart Layers: A New Era in Visualization 11:51 Tableau Server vs. Tableau Cloud: The Future 13:19 Tab Move: Streamlining the Transition to Cloud 15:05 Accessibility Features in Tableau 15:18 Composable Data Sources: Unlocking New Potential 20:47 Optimizing Data Integration in Tableau 22:17 The Future of Composable Data Sources 24:22 Leveraging AI for Data Management 26:30 The Role of AI in Data Exploration 26:31 Understanding Agentic Analytics 29:52 Implementing Tableau MCP Effectively 34:24 Designing for Consistency in Data Visualization 36:54 Evolving Data Governance and Ethics 38:31 Exploring New Features in Tableau 39:08 The Future of Data Integration and Collaboration 48:26 Building a Data-Driven Organization 50:41 The Role of Data Leaders as Consultants   Links Kirk Munroe on LinkedIn: https://www.linkedin.com/in/kirkmunroe/ Candi Munroe on LinkedIn: https://www.linkedin.com/in/candimunroe/ Paint with Data: https://paintwithdata.com Paint with Data Blog: https://www.paintwithdata.com/blog Data Modeling with Tableau by Kirk Munroe: https://www.amazon.com/Data-Modeling-Tableau-practical-building/dp/1803248025 Kirk Munroe on the Flerlage Twins blog: https://www.flerlagetwins.com/2023/01/data-.html TC26 Devs on Stage: https://www.salesforce.com/plus/experience/tableau_conference_2026/series/tableau_conference_2026_highlights/episode/episode-s1e25 Watch TC26 on demand: https://www.salesforce.com/plus/experience/tableau_conference_2026 Fi Crocker on LinkedIn: https://www.linkedin.com/in/ficrocker/ Sarah Burnett on LinkedIn: https://www.linkedin.com/in/sezbee/ Keywords Tableau Conference 2026, TC26, Tableau Solve, composable data sources, agentic analytics, Tableau MCP, Viz layers, chart layers, write-back Tableau, Tableau Visionary, data governance, Tableau Cloud, Tableau Server, AI in Tableau, semantic layer Tableau, data leadership, data leaders, Paint with Data, Kirk Munroe, Candi Munroe, Tableau data modelling, headless BI, Slack analytics, Tableau Desktop Authoring API, row-level security, Tableau prep, published data sources, Dub Dub Data, unDUBBED podcast

Summary Fresh off the stage at Tableau Conference 2026, Sarah Burnett and Fiona Crocker bring their live TC26 session straight to the unDUBBED audience. Their session was called Building a Data Culture with Datafluencers, and the whole premise is this: the best data work in most organisations will never be seen - not because it isn't good enough, but because nobody told the story. They walk through why brilliant work disappears, who is responsible for fixing it, and the practical tools to start changing that this week. The centrepiece is the seven-part data story arc, walked through using Freshmart, a fictional wholesale food chain where waste was above target despite accurate data and capable teams.  Whether you're a data analyst whose work isn't getting the recognition it deserves, a manager trying to build a data culture, or someone who's ever built a dashboard that nobody used - this episode is for you.   Takeaways The gap between delivery and adoption is where great data work disappears - and it's a storytelling problem, not a data problem Great work goes invisible for three reasons: no channel, no story, and no leaders to promote it A Datafluencer isn't a job title - it's a set of behaviours that makes work visible, shareable, and heard The seven-part data story arc gives a repeatable structure: hook, problem, insight, solution, result, learning, call to action One dashboard reframe - not a rebuild - saved Freshmart 2.4 tonnes of waste and $672,000 in annualised costs in six weeks A public data wins channel builds culture over time - people stop lurking and start contributing when the space feels worth being in Amplification is the step most people skip - make it one click for your manager, then prompt them to take it higher   Chapters 00:00 Introduction to Data Culture and Datafluencers 01:24 Understanding Why Data Work Goes Invisible 03:19 The Role of Datafluencers in Amplifying Work 05:39 Framework for Effective Data Storytelling 08:10 Building a Winning Channel for Data 10:56 Amplification and Cultural Impact of Data 13:43 Conclusion and Call to Action   Links Data Fluencer Toolkit - https://www.dubdubdata.com/offers/iU2dFRBd/checkout Fi Crocker on LinkedIn - https://www.linkedin.com/in/ficrocker/ Sarah Burnett on LinkedIn - https://www.linkedin.com/in/sezbee/   Keywords data culture, Datafluencer, data storytelling, data visibility, Tableau Conference 2026, TC26, data story arc, dashboard adoption, seen heard promoted, data leadership

In this episode of unDUBBED, we’re joined by Cole Nussbaumer Knaflic, founder of Storytelling with Data and one of the most influential voices in data storytelling and data visualisation. Cole shares practical, real-world insights on why data often fails to drive decisions - even when the analysis is right - and how to shift from simply showing data to actually influencing outcomes. From her journey through banking and Google’s People Analytics team to building a global data storytelling movement, this conversation is packed with actionable techniques to help you communicate data with clarity, purpose, and impact.   🔑 Main Topics Why data fails to drive decisions - and how to fix it The shift from showing data to explaining data Audience-first thinking in data storytelling Structuring communication using the narrative arc The role of iteration, feedback, and time in crafting data stories When to use dashboards vs storytelling The risks and realities of AI in data visualisation   📌 In This Episode Cole’s journey from banking to Google and founding Storytelling with Data How data visualisation became a critical business communication skill Why designing for yourself (not your audience) is the biggest mistake in data The difference between exploring data vs explaining data How to use titles, annotations, and contrast to guide attention The power of low-fidelity prototyping and iteration for alignment Why most data presentations follow a “selfish” structure - and how to fix it Using the narrative arc to create tension and drive action How to develop a clear “Big Idea” before building anything The role of AI in data storytelling - speed vs risk, and hallucinations Common chart mistakes, including unnecessary detail and poor audience focus ⏱️ Chapters 00:00 - Introduction to data storytelling with Cole Nussbaumer Knaflic 02:12 - Cole's journey from banking to data visualization at Google 03:45 - The importance of clear visual communication in business 06:46 - Teaching data visualization at Google and creating impact 08:45 - The storytelling ecosystem: books, workshops, community 10:48 - Making data concepts accessible and engaging 13:33 - The collaborative process behind “Storytelling with Data: Before and After” 17:17 - The power of the “before and after” in data improvement 18:10 - The storytelling process as a “glow up” for data 19:01 - Tools and techniques for rapid prototyping and iteration 20:41 - Strategies for understanding the audience and customizing communication 23:52 - Using low-tech methods for stakeholder alignment 27:03 - Balancing detail with clarity: avoiding overwhelm 29:54 - Differentiating between storytelling and dashboards 33:10 - Structuring data communication as a narrative arc 36:39 - The importance of concise messaging and annotations 46:16 - Layering information to enhance understanding 50:52 - Developing the “Big Idea”: clear, impactful core message 55:43 - The role of AI and machine-generated charts in modern data storytelling 64:10 - Common chart crimes and things to stop doing 64:47 - Final takeaways: prioritize audience understanding and time 66:09 - Resources, books, and ways to connect with Cole   🔗 Resources & Links Storytelling with Data → https://www.storytellingwithdata.com Storytelling with Data: Before and After (book) → https://www.storytellingwithdata.com/before-and-after Daphne Draws Data → https://daphnedrawsdata.com Tableau → https://www.tableau.com Figma → https://www.figma.com AI tools → https://claude.ai   | https://chat.openai.com   🔗 Connect with Cole Nussbaumer Knaflic LinkedIn → https://www.linkedin.com/in/colenussbaumerknaflic Website → https://www.storytellingwithdata.com 🧠 Key Takeaways Data alone doesn’t drive decisions - clear communication does If your audience doesn’t know what to do, your data has failed Start with the audience and the action, not the analysis Use storytelling to create tension, clarity, and direction AI can accelerate workflows - but human judgment remains critical