While insights certainly measure the past, data can also be used to predict the future. Chris Kunze is the Founder and President of Kunze Analytics where he assists decision-makers, sales leaders, HR professionals, trainers, and coaches in order to predict employee performance, retention, engagement, and safety. Chris is a self-proclaimed Data Geek who dives into the data to find ‘hidden talent.’
Let’s get a bit technical today and learn from Chris how he uses the Predictive Model Markup Language used to create and run models in different software packages.
[00:01 - 05:18] Opening Segment
- Let’s get to know Chris Kunze
- Chris gives some background on himself
- Chris talks us through some of the in and outs of PMML
[05:31 - 07:41] Why PMML is Useful
- The value and importance of having a common language
- Automating your process
[07:55 - 23:39] The Most Predictive Data for Employee Performance
- PMML’s usefulness in assessment data
- Chris talks about creating and running models
- Reliable data designed to be used for serious decisions
- Finding hidden traits; Chris’ example
- Chris’ advanced statistical modeling test set
[23:53 - 32:36] Coaching; Insights from People Analytics
- Chris talks about his experience with training students
- Pulling out people’s greatest strengths
- The ability to monetize your work
[32:37 - 34:51] Closing Segment
- Summarizing our discussion today
- Final Words
Tweetable Quotes:
- “This [PMML] is a way the industry can standardize concepts, and provide some transparency…” - Chris Kunze
- “What gets measured gets managed, if you don’t know what your measurements are you don’t know where you stand or where you can make and track improvements.” - David Turetsky
Connect with Chris on LinkedIn. Visit https://www.kunzeanalytics.com to learn more about his space.
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Connect with me:
Email: david@turetskyconsulting.com
This show is brought to you by Turetsky Consulting LLC, our company providing business consulting on Analytics, HR Processes, and Rewards with a focus on getting answers that organizations need by demystifying People Analytics.
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