HR Data Labs

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Unlock the future of HR, today with the HR Data Labs podcast! Dive into transformative insights, expert interviews, and cutting-edge practices that empower organizations to harness their workforce’s potential. Join us for engaging discussions that will inspire you to innovate, strategize, and lead with confidence! Tune in now!

  1. Data Governance in HR is NOT Optional!

    4일 전

    Data Governance in HR is NOT Optional!

    In this episode, we dive deep into the challenges and opportunities of HR data governance, exploring how organizations can improve data quality, ownership, and usability in a rapidly evolving AI landscape. Join us for practical insights from seasoned HR analytics experts on building a data-driven culture that supports strategic decision-making. Key Topics: Why HR data is often unreliable and the impact on decision-making The role of ROI and cultural mindset in improving HR data quality The importance of ownership, stewardship, and clear definitions in data governance How AI and machine learning magnify data quality issues if governance is lacking Practical steps to start building your HR data governance framework The critical role of documentation, data catalogs, and system integration Common pitfalls: managing multi-system data consistency and avoiding errors Quick wins: focusing on key metrics and stakeholder collaboration Timestamps: 00:00 - Introduction: Why HR data governance matters today 02:30 - Challenges HR faces with data quality and accuracy 06:15 - Why organizations struggle to demonstrate ROI from HR data 09:00 - Cultural and mindset barriers to effective data management 11:00 - The impact of AI and machine learning on HR data quality 12:30 - Context and system integration challenges across HR tech stack 15:11 - Defining HR data governance: Ownership, stewardship, and quality 17:00 - Creating a data glossary and system of record for HR data 19:05 - Real-world examples of poor HR data visibility and audit issues 21:00 - Using chatbots and AI: risks, benefits, and data consistency 24:00 - The importance of documentation and version control in AI applications 27:40 - Practical steps to start your HR data governance journey 30:00 - The significance of aligning metrics and defining owners 33:00 - Building a culture of data excellence and quick wins 36:00 - Addressing expectations for pristine data and managing realities 37:00 - Final recommendations for HR leaders to improve data governance Connect with Guests: Raswinder Singh - LinkedIn | Twitter Ankit Abrol - LinkedIn | Twitter

    40분
  2. Season 10 Recap

    1월 22일

    Season 10 Recap

    🚀 Dive into the future (at least the next 6 months) of HR with the latest episode of the HR Data Labs podcast! In the Season 10 recap, host David Turetsky explores the evolving landscape of compensation, strategy, and people analytics. We recap the current Season 10, where we discuss addressing training gaps, the impact of AI in HR, and other cool episode topics. Then we review our prediction for the past 6 months and make bold predictions for first 6 months of 2026. Join us for a thought-provoking discussion that prepares you for the challenges and opportunities ahead. #HRInnovation #FutureOfWork #HRDataLabs

    25분
  3. What's to come in Compensation in 2026

    1월 15일

    What's to come in Compensation in 2026

    Join host David Turetsky and guest Paul Reiman from Novo Insights as they delve into the dynamic world of compensation strategies and pay transparency. Discover how regulatory changes in the US and Europe are shaping the landscape, the critical role of job documentation, and the impact of technology in fostering transparency. Tune in for insightful predictions for 2026 and learn how political climates may influence future pay transparency laws. Chapters Introduction and Guest Introduction Compensation Strategies and Pay Transparency Regulatory Changes in the US and Europe The Role of Technology in Transparency Predictions for 2026 Conclusion and Final Thoughts Quotes: "Job documentation is crucial for pay transparency." "Regulatory changes are reshaping the compensation landscape." "Expect a shift in compensation survey methodologies by 2026." "Technology plays a key role in enabling transparency." "Implementing pay transparency presents unique challenges." "2026 will bring significant changes to HR practices." "Adapting to regulatory changes is essential for success."

    42분
  4. How Skills Data is Transforming HR into True Business Partners

    1월 8일

    How Skills Data is Transforming HR into True Business Partners

    Craig Friedman, Talent and Skills Transformation Leader at St. Charles Consulting Group and author of Enterprise Skills Unlocked, joins us this week to discuss the shift toward skills-based organizations. He breaks down how data-driven transformations allow companies to move from simple headcount management to true capability management. Craig also shares practical advice on how to prioritize skills projects to ensure they solve real business problems and deliver ROI. [0:00] Introduction Welcome, Craig! Today’s Topic: How Skills Data is Transforming HR into True Business Partners [5:05] What does a skills-based transformation look like in practice? Shifting the talent process from an exercise in headcount management to an exercise in capability management. Moving away from static "boxes on an org chart" to using granular data that supports the entire talent infrastructure. Leveraging skills data that lives in both business systems (capabilities) and people systems (individual skills) to better align with business functions. [11:57] How different teams leverage skills data differently Why L&D teams need granular skill details, while staffing teams prioritize context on scope and scale for compensation purposes. The importance of creating an enterprise data taxonomy where different departments can agree on a skill but append their own metadata. Using machine learning to handle the searches, connections, and adjacencies required to make the data useful across teams. [26:14] The impact on Learning and Development (L&D) How real-time skills gap analysis simplifies curriculum redesign when jobs or organizational structures change. The growing need for assessment and validation to verify skills learned through informal methods like coaching or on-the-job experience. Identifying business cases where skills can make a clear difference and prioritizing them based on value and risk. [36:20] Closing Thanks for listening! Quick Quote “A lot of the reason we're doing this now when we couldn't do it before is because of these more advanced tools in data analytics and AI and machine learning that actually help us manage data at that scale.” Link to Craig's book: https://a.co/d/0naqmvh

    39분
  5. Holiday Reflections and New Beginnings

    2025. 12. 18.

    Holiday Reflections and New Beginnings

    🚀 Exciting changes are coming in 2026! Join David Turetsky as he shares his vision for HR Data Labs, focusing on positivity and growth. Discover how we're merging brown bag lunches into our podcast and launching something new for HR Data Labs. #HRDataLabs #2026Vision #Positivity Chapters 00:00:34 Introduction and New Directions 00:01:42 David Turetsky's Personal Goals 00:03:59 HR Data Labs Evolution 00:06:19 HR Data Doodles news

    10분
  6. Why Hybrid Work is Still a Mess

    2025. 12. 11.

    Why Hybrid Work is Still a Mess

    Ranya Nehmeh, HR Strategist and Adjunct Professor at FHWien der WKW in Vienna, Austria, and Peter Cappelli, Professor of Management and Director of the Center for Human Resources at the Wharton School, join us this week to discuss some of the topics covered in their book, In Praise of the Office. We explore the current tumultuous state of Return-to-Office (RTO) mandates, why "hybrid" work is often failing to deliver on its promises, and the critical need for intentional management to foster human connection. [0:00] Introduction Welcome, Ranya and Peter! Today’s Topic: The Realities of Hybrid Work [9:15] The messiness of Return-to-Office (RTO) today Why the media narrative often contradicts the realities of small business data. Why the definition of “hybrid” varies per organization. [19:03] Is work actually getting done remotely? Distinguishing between hitting individual KPIs and maintaining organizational health. The deterioration of meeting culture and the rise of "cameras off" apathy. The loss of social norms and the difficulty of resolving conflict without face-to-face interaction. [29:50] Do policies need to change for the new world of work? Addressing proximity bias and its impact on promotions and career development. Why treating hybrid work the same as traditional office work is a management failure. Understanding the winners and losers of remote work, particularly for younger or newly onboarded employees. [46:23] Closing Thanks for listening! Quick Quote “If you really want people to come back into the office, you have to do it with intentionality.”

    50분
  7. Why US Executive Pay Leads the Global Market

    2025. 12. 04.

    Why US Executive Pay Leads the Global Market

    Steve Brink, CEO of GECN Group, joins us this week to discuss the complex world of executive compensation. Steve breaks down why the US pays a premium compared to the rest of the world, how boards are shifting their focus from simple compensation to holistic talent management, and the global trends driving executive pay structures today.

    37분
  8. Maintaining Personal Agency Through AI Integration

    2025. 11. 20.

    Maintaining Personal Agency Through AI Integration

    Bob Pulver, host of the Elevate Your AIQ podcast and a 25-year enterprise tech and innovation veteran, joins us this week to unpack the urgent need to move past "AI" as a buzzword and define what "Responsible AI" truly means for organizations. He shares his insights on why we are all responsible for AI, how to balance playing "defense" (risk mitigation) and "offense" (innovation), and why we must never outsource our critical thinking and human agency to these new tools. [0:00] Introduction Welcome, Bob! Today’s Topic: Defining Responsible AI and Responsible Innovation [12:25] What Does “Responsible AI” Mean? Why elements (like fairness in decision-making, data provenance, and privacy) must be built-in "by design," not bolted on later. In an era where everyone is a "builder," we are all responsible for the tools we use and create. [25:48] The Two Sides of Responsible Innovation The "responsibility" side involves mitigating risk, ensuring fairness, and staying human-centric—it’s like playing defense. The "innovation" side involves driving growth, entering new markets, and reinvesting efficiency gains—it’s like playing offense. [41:58] Why don’t we use AI to give us a 4-day work week? The critical need for leaders to separate their personal biases from data-driven facts. AI’s role in recent layoffs. [50:27] Closing Thanks for listening! Quick Quote “We're all responsible for Responsible AI, whatever your role is. You're either using it or abusing it . . . or you're building it or you're testing it.”

    53분
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Unlock the future of HR, today with the HR Data Labs podcast! Dive into transformative insights, expert interviews, and cutting-edge practices that empower organizations to harness their workforce’s potential. Join us for engaging discussions that will inspire you to innovate, strategize, and lead with confidence! Tune in now!

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