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HR Data Labs

HR Data Labs

By: WRKdefined Podcast Network
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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!All rights reserved by WRKdefined Career Success Economics Management Management & Leadership
Episodes
  • Steven Rothberg - The Evolution of Recruitment Metrics & The Future of AI in HR

    Steven Rothberg - The Evolution of Recruitment Metrics & The Future of AI in HR
    Feb 26 2026
    Are you tired of dealing with outdated recruiting strategies? Wondering if AI can truly revolutionize hiring? This episode dives into the gritty truth about how modern metrics and emerging AI tools are reshaping talent acquisition—sometimes for better, sometimes for worse.Join us as we unpack decades of recruiting wisdom, the pitfalls of cost-per-click, and what’s next in HR tech. In this episode: The history of traditional job advertising and its limitations How cost-per-application models are changing the game The messy reality of defining "qualified" applications and why subjectivity matters The myth of AI as an unbiased recruiter and its legal risks Why transparency in pay and job descriptions can fix much of the hiring chaos The dangers of scaling bad processes and how to avoid scaling mistakes Practical insights on choosing the right metrics for your hiring needs The future of AI in recruitment and the importance of process over technology Useful links: College Recruiter Work Defined Podcast Network Ultimate Guide to Recruitment Metrics Artificial Intelligence in Hiring – Industry Report The Future of Pay Transparency Reach out to Steven Rothberg: LinkedIn Twitter Timestamps: 0:00 - Welcome and episode overview: Are recruitment metrics still holding up?00:42 - Introducing Stephen Rothberg and college recruiter’s journey01:14 - The roots of traditional recruiting: From flyers to newspapers02:25 - Starting a business from a summer job and risk-taking mentality03:52 - The Winnipeg Jets story: Childhood risks and lessons in boldness05:23 - The shift from inefficient to performance-based ad models06:49 - The real meaning of "click" and the confusion across platforms08:13 - The rise of cost per application and its emotional impact on employers09:54 - Defining "qualified applications"—subjectivity and its pitfalls11:35 - Why scaling bad processes makes them worse13:08 - The evolution of pay-per-click and undisclosed definitions14:54 - The deadly cycle of bad job descriptions and reposting16:23 - How hiring for "fit" often misses the mark and costs real money17:10 - Qualification in hiring: Who judges? The subjectivity of recruiters18:29 - Scaling broken processes: The airplane manufacturing example19:54 - The quiet cost of bad candidate sourcing: The ATS funnel22:17 - The dangerous hype around AI and its current limitations in hiring23:12 - The black hole of AI decision-making and lack of transparency26:24 - The importance of honest job descriptions and pay transparency28:43 - Do employers prefer CPC, duration-based, or new models?30:01 - The chase for quality candidates and the costs involved31:27 - How to effectively invest in recruitment—long-term wins over shortcuts32:28 - The impact of AI on assessments and candidate matching35:55 - The legal peril of AI-driven hiring decisions and audit trail challenges37:25 - Final thoughts: Embracing process, transparency, and smart metrics
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    39 mins
  • Brian Platz - The Future of AI in HR: Privacy, Security, and Transformation Opportunities

    Brian Platz - The Future of AI in HR: Privacy, Security, and Transformation Opportunities
    Feb 19 2026
    In this episode, we explore how artificial intelligence is revolutionizing HR, with a focus on building trust through data privacy and security. Join us as we discuss practical steps, emerging challenges, and the evolving role of HR professionals in the AI era. Key Topics: The importance of foundational data quality before implementing AI in HR Securing sensitive employee data and managing privacy concerns The role of semantic layers and data organization for effective AI use How AI impacts HR workflows and transforms knowledge work Practical approaches to integrating AI responsibly and securely Education needs for HR to understand AI risks and opportunities Future trends: AI's potential to reinvent HR practices, not just automate Resources & Links: Fuel 50 - Workforce Mobility and Talent Pipelines Amazon - Book: Data Privacy and Security in the Cloud Flurry - Official Website Amazon Bedrock - AI Model Service Anthropic - AI Safety and Privacy Guarantees OpenAI - Responsible AI Use Connect with Brian Platts: LinkedIn Twitter Timestamps: 00:30 - Welcome and introduction to the episode 01:15 - Brian Platts’ background in HR and software 02:08 - Flurry’s mission to make data meaningful for HR 03:26 - Fun fact: starting career driving a semi truck 04:44 - AI in HR: privacy, security, and data foundations 05:53 - Preparing your HR data for AI adoption 06:08 - Challenges with data quality and use cases 07:08 - Security considerations: private vs. public data 08:22 - Trusting AI vendors and data-sharing risks 09:15 - Teaching AI to query data securely 10:07 - Data organization and semantic layers 11:29 - Improving chatbots and avoiding misinformation 12:26 - Ensuring process accuracy and data integrity 13:14 - Sharing vs. protecting employee data 14:05 - Re-implementing permissions in AI-driven systems 15:01 - Education and awareness around AI security 16:13 - Learning from SaaS security issues during early cloud adoption 17:18 - HR’s role in AI education and safeguarding IP 18:14 - Balancing productivity gains with security controls 19:06 - AI’s impact on HR future: automation and new workforce roles 20:16 - The concept of the “Meat Layer” and human-AI collaboration 21:02 - Will AI replace HR jobs or empower them? 22:16 - The limits of current AI technology and future innovations 23:03 - Analogies: AI as a horse and the importance of tooling 24:06 - Embracing AI to enhance human work rather than replace it 25:16 - Reinventing HR processes beyond IT-led automation 26:18 - Regulatory challenges and incremental HR AI adoption 27:30 - How HR can lead responsible AI integration 28:03 - Final advice for HR professionals: think broadly and connect the dots
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    30 mins
  • Raswinder Singh & Ankit Abrol - Data Governance in HR is NOT Optional!

    Raswinder Singh & Ankit Abrol - Data Governance in HR is NOT Optional!
    Feb 12 2026
    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
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    40 mins
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