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In the movie Moneyball, data revealed that baseball’s traditional scouting system was flawed. It challenged long-standing beliefs, exposed inefficiencies, and forced uncomfortable conversations. People Analytics teams are capable of doing the same thing... We celebrate strong insights that drive efficiency, performance, and business impact. But what happens when those insights challenge the status quo? For example, what if the data shows: - That top performers aren’t the ones getting promoted? - A fan-favorite leadership program doesn’t actually work? - Bias and favoritism in hiring, pay, or promotions? - A key strategic initiative is failing? These aren’t just hypothetical scenarios. This actually happens. And when they do, the response isn’t always positive. These can be very challenging situations to be in. This is one of my favorite questions to ask candidates when they're interviewing for a PA role: What do you do when the data provides evidence that leadership teams do not want to see? How have you managed these scenarios? At the end of the day, People Analytics isn’t just about uncovering insights. We are working on strategizing what happens next. Feel free to drop your thoughts in the comments! #PeopleAnalytics #Leadership #DataTransparency #HR #FutureOfWork #ToughConversations

Just wrapped up an incredible panel discussion on leveraging data and analytics for strategic talent optimization! This is one you don't want to miss. What an opportunity to have such a thought-provoking conversation with industry experts including: - Shekar Nalle Pilli Venkateswara( NV): Workforce Transformation Practice Leader at Willis Towers Watson - Ying Li: Global Head of People Analytics at PepsiCo Check out the full discussion below. Cheers all and Happy Thursday :)

At Cinemark I had a rare opportunity to lead the Voice of the Customer program while also spearheading an employee innovation program called Cinevations. Cinevations provided an opportunity for employees to submit ideas to improve the customer experience and business overall. When I analyzed employee submissions alongside customer feedback, I discovered many areas of alignment. For example, some customers complained about unclean restrooms on Friday and Saturday nights. At the same time, employees submitted ideas on how to maintain cleaner theatres during peak hours. Another common customer pain point was long lines. Simultaneously, employees reported that the mobile app screen was too dark, making it difficult to scan digital tickets, which contributed to longer lines. By connecting these insights, we identified overlapping concerns and had an opportunity to prioritize quick wins and high impact solutions. In fact, I think IT brightened the mobile app screen within 24-48 hours of getting the insight. It was incredible. What's the takeaway here? If it makes sense for your organization, employee experience and customer experience teams should collaborate. The datasets should be friendly with one another. Trust me....the insights you uncover could drive meaningful improvements for both employees and customers.

Hey all! On April 9th at 1pm EST the People Analytics Exchange goes virtual. Hear from industry experts from Target, iHeartMedia, Yahoo, Labcorp, Kenvue, and more. 🔗 Register Now: https://lnkd.in/ggwDYh2R A few important areas we cover include: - Transforming HR through data-driven insights - Evolving into a strategic business partner - Driving desired business outcomes in the age of AI Don't miss this opportunity to gain actionable insights and stay ahead in the evolving world of People Analytics. Cheers all! #PeopleAnalytics #HRTransformation #HRStrategy #FutureofWork

Exciting milestone! I just finished writing my first children’s book. As I move into the illustration phase, I'm bumping into something I didn’t expect. There are MANY new "illustrators" who popped up in the last 6-12 months, with their portfolios showing AI flaws like wonky hands, mismatched details, blurred faces, and more. It got me thinking…most discussions about GenAI seem to focus on speed and efficiency, but quality is just as important. Leaders must set clear expectations that GenAI isn’t just for faster work. Communication should emphasize its purpose is also for BETTER work. Teams should produce top-quality outputs through experimentation and refinement, and those who leverage AI to create excellent results will stand out! The box-checking, low quality efforts are easy to spot. P.S. My book is on track for a Summer release, pending smooth publishing. Wish me luck! #ChildrensBooks #Illustration #AI #Creativity #FutureOfArt

Very cool! Spotify launched an HR people analytics solution. HR driving revenue… :::boom::: Their approach makes it easier to access data while also creating a culture where decisions are influenced by rapid insights. I’d call this “decision engineering” at scale. Nice to see entrepreneurial thinking like this! Visionary leadership. What a way to innovate and empower team members. See the link below. Thx for sharing Benny J