Hi everyone,

I'm currently working on an enterprise while studying Data Science (bachelor level) with software engineering bacckground in Barcelona, and looking to grow my experience by identifying some realistic People Analytics use cases I could explore at work. I work in a large company, and while I don't have much hands-on experience yet, I'm very motivated to start contributing with small but meaningful projects.

I'm particularly interested in ideas that involve:

• Data exploration to uncover hidden patterns, like indicators of employee turnover, absenteeism, or team dynamics.

• Predictive approaches, like forecasting churn, performance, or engagement issues.

• Quick wins something that could be done with relatively accessible data and limited resources, but still offer value.

• Anything with potential for real business impact, even if small at first.

Would really appreciate any suggestions, examples, or even papers/resources that helped you when you were starting out. What I would really like to avoid is starting projects that are unrealistic / dead ends from day 1.

Thanks in advance!

Hi Jack,

Thanks so much for sharing your experience and goals - your message really resonated with me. I recently graduated with a degree in Information System Management, building on a background in Software Engineering, and I'm currently working in a system tech environment while exploring ways to contribute meaningfully through analytics.

Like you, I'm very motivated to uncover practical People Analytics use cases - whether it's detecting early signs of turnover, analyzing absenteeism trends, or improving team-level engagement insights. I'm especially drawn to quick wins that can be executed with limited data and resources but still deliver clear business value.

It would be great to exchange ideas or even collaborate on a lightweight proof of concept - aiming for real-world impact without overcomplicating things - and I'd love to stay in touch as we navigate these early steps in applying data science to the workplace.

Looking forward to connecting!

Best regards,
Vinay Reddy

What should be done?=)

Hi Jack,

Thanks again for your response - I wanted to briefly share one project idea I'm currently working on as part of an internal innovation initiative at IBM.

It's called "TestLab Advisor" - an AI + data tool designed to help mainframe test engineers troubleshoot smarter. The idea is to input things like refcodes, FRU calls, and recovered error messages, and then get:

• The affected slot/drawer

• What action to take (e.g., reuse vs replace)

• SE terminal commands to minimize rework

• A log of test inefficiencies (e.g., repeated card testing or unused hooks)

It's built with watsonx.ai, Python/Streamlit, and some simulated logs from my current test environment. I'm also working on basic analytics (what FRUs are most reused, time saved by skipping retests, etc.).

It's more on the technical side of People Analytics, but I'm really interested in using this as a foundation to explore how testing behavior, hardware reuse, and decision-making could be optimized over time.

Would love your thoughts - or if you think it's a stretch from what you're aiming at, I'm happy to adapt it!

Best regards,
Vinay Reddy