How I Tackled GenAI-Powered Data Analytics (And Unlocked a New Perspective on AI Strategy)
Source: Dev.to
My Journey into Tata’s GenAI‑Powered Data Analytics Simulation
After completing the Commonwealth Bank Software Engineering Challenge and my AWS Solutions Architect journey, I was hungry for the next one. That’s when I discovered Tata’s GenAI‑Powered Data Analytics simulation on Forage.
“Comfort is the enemy. Keep moving.”
Below is my story of how I tackled a real consulting scenario—predicting delinquency risk, designing ethical AI systems, and building an end‑to‑end GenAI‑powered analytics solution.
Want to try it yourself? Check out the simulation here before reading. SPOILER ALERT ahead!
The Scenario: AI Transformation Consultant
The simulation places you in the role of an AI transformation consultant working with Geldium Finance’s collections team.
| Client | Problem | Goal |
|---|---|---|
| Geldium Finance | High delinquency rates, inefficient collections, no AI strategy | Design a GenAI‑powered analytics solution for predicting delinquency risk and building an ethical, scalable collections strategy |
This wasn’t just another theoretical exercise—it was about impact.
The Challenge: Three Interconnected Problems
What I loved about this simulation was that the tasks weren’t compartmentalized. Each built on the previous one, mirroring how real consulting work actually flows.
Task 1 – Exploratory Data Analysis (EDA) with GenAI
- Dataset: Real customer financial data with delinquency flags.
- Goal: Conduct an EDA using GenAI tools to assess data quality, identify risk indicators, and structure insights for predictive modeling.
Instead of spending hours staring at correlation matrices, I used GenAI as a thinking partner—Claude and ChatGPT helped me:
- Structure hypotheses
- Identify outliers
- Surface hidden patterns
Mindset shift: GenAI isn’t about replacing analysis; it’s about amplifying insight generation at scale.
Task 2 – Designing a Predictive‑Modeling Framework
- Goal: Build an initial no‑code predictive‑modeling framework to assess customer delinquency risk.
No‑code forced me to think about business feasibility, scalability, and explainability before writing a single line of code. My framework leveraged GenAI to:
- Define logic for risk scoring without complex algorithms
- Create transparent, auditable decision pathways
- Generate evaluation criteria aligned with business goals
- Embed regulatory‑compliance considerations from day one
Key takeaway: The best models are often the ones non‑technical stakeholders actually understand and trust.
Task 3 – Architecting an AI‑Driven Collections Strategy
- Goal: Design a comprehensive collections strategy that:
- Utilises agentic AI (autonomous AI agents)
- Incorporates ethical AI principles and fairness considerations
- Meets regulatory‑compliance requirements
- Scales across thousands of customers
I wrestled with questions such as:
- How do you design AI automation that reduces bias rather than amplifies it?
- What does a scalable implementation framework actually look?
- How do you balance aggressive collections with customer empathy?
The deliverable wasn’t a 200‑page architecture document; it was a thoughtful, actionable strategy that balanced business needs with ethical responsibilities.
Why This Challenge Hits Different
| # | Reason |
|---|---|
| 1 | Real‑World Messiness – Dirty data, misaligned requirements, contradictory constraints forced trade‑offs and justification, just like on the job. |
| 2 | GenAI Integration (Not AI Replacement) – The question was “how do I use AI tools to solve a business problem?” rather than “how do I build an AI solution?” |
| 3 | Ethical Complexity – Collections is sensitive; the simulation demanded fairness, bias mitigation, and regulatory awareness. |
| 4 | Progressive Scaffolding – Each task built naturally on the previous one, creating a realistic consulting engagement flow. |
| 5 | Forage’s Presentation – Polished, professional, with realistic client emails and plausible scenarios, turning a training exercise into a portfolio‑worthy piece. |
What I Built
| Deliverable | What It Does |
|---|---|
| EDA Summary Report | Data‑quality assessment, risk‑indicator identification, structured insights |
| Predictive‑Modeling Framework | No‑code risk‑scoring logic with transparent decision pathways |
| Collections Strategy | Ethical AI architecture, implementation roadmap, regulatory alignment |
| Streamlit Application | Interactive dashboard for EDA and model planning |
Tech Stack
- Python + Pandas – Data wrangling
- Streamlit – Interactive dashboard
- GenAI (Claude / ChatGPT / Grok) – Thinking partners throughout the workflow
- Markdown – Structured documentation
👉 Open‑Source Code (GitHub):
GitHub Repository
Key Takeaways
- Start with the business problem – Every model decision should trace back to impact.
- GenAI amplifies, doesn’t replace – Use it as a thinking partner, not a crutch.
- Explainability > Complexity – The best models are the ones stakeholders trust.
- Ethics aren’t optional – Fairness and compliance must be baked in from day one.
- Ship something real – I didn’t just write reports—I built a working Streamlit app.
Try It Yourself
Then come back and tell me:
- What surprised you most?
- How did your approach to analysis shift?
- What ethical dilemmas did you wrestle with?
I genuinely want to hear your takes. The beauty of challenges like this is there’s no single right answer—just thoughtful problem‑solving.
Potential Next Steps
| Enhancement | Description |
|---|---|
| Advanced Visualizations | More sophisticated Streamlit dashboards |
| ML Model Implementation | Validate the no‑code framework with actual models |
| Ethical AI Documentation | Lessons learned in bias mitigation |
| Prompting Strategies | Deep dive into GenAI techniques that worked |
Final Thoughts
This project stretched me across roles: data analyst, ML strategist, consultant, and engineer. But that’s the point—real problems don’t come in neat boxes.
I walked away with a working application, solid documentation, and a sharper perspective on how GenAI fits into enterprise analytics. That’s the kind of outcome I bring to every engagement.
Go give it a shot. I’ll be watching for your takes in the comments. 🚀