Journey : Analytics to Product

4 MINS

# Journey : Analytics to Product

MICA’s PGDM-C in Marketing Analytics shaped the way I entered product management through a strong data-first mindset. Over time, this perspective has become integral to how I think about users, decisions, and outcomes.

Data as Foundation

Analytics training means starting every product discussion with questions:

What does the data tell us?

How will we measure success?

What's our baseline? This may seem obvious, but it’s surprisingly rare in practice. Starting with data prevents intuition-led decisions from being retrospectively justified with selective evidence. It enforces intellectual honesty and leads to better, more defensible decisions.

The Fossil Years

At Fossil Group, I built tools where data was the product. The Ideal Weeks of Supply model shifted the conversation toward a customised approach, moving away from a rigid, one-size-fits-all yardstick. The Inventory Health Dashboard tracked supply chain metrics. The Regional Sales Dashboard monitored trends across APAC.

Building tools for data users taught me:

Data presentation matters as much as data accuracy

Actionable insights beat comprehensive reporting

Speed of access determines usage A beautiful dashboard that nobody opens is worthless. A basic view that drives daily decisions is invaluable.

Scrum Through an Analytics Lens

Getting certified as both Scrum Master and Product Owner while having an analytics background created an interesting perspective.

I track sprint metrics obsessively:

Velocity trends

Bug rates by release

Estimation accuracy But I also know metrics can be gamed . The qualitative side i.e. team health, stakeholder satisfaction, user feedback matters just as much.

Healthcare Analytics

At CureBay, analytics takes on new meaning. We're not optimizing for engagement or revenue, we're measuring health outcomes and access.

How much did order response time decrease?

Did alternative medicine suggestions reduce sales loss?

How to remove weak links in the supply chain? These metrics impact directly whether patients get their medicines. That changes how seriously you take data quality.

The Quantitative-Qualitative Balance

Product management demands both quantitative rigor and qualitative empathy. Data comes naturally to me; empathy is built through feedback, reflection, and deliberate practice.

Numbers show what is happening, stories explain why , and people reveal who benefits and how. Strong product decisions emerge from triangulating all three.

My analytics background initially led me to over-index on data, but experience has taught me to validate insights through user conversations and stay open to signals that don’t fit neatly into a spreadsheet.

Building Teams

Not everyone needs to run SQL, but everyone needs to think data-first. Leading teams has taught me to balance this with a people-first mindset aligning individual aspirations with collective goals. Open conversations about career ambitions, translated into clear and actionable plans, create a strong sense of ownership and long-term fulfillment within teams.

A culture of asking “How do we know?” and “How will we measure it?” compounds over time, and it’s one of the most valuable strengths an analytics background brings to effective product leadership.

On-Ground Adoption: Solving the Last Mile Problem

Siddharth skipped presentations and built real AI products.

Siddharth Chauhan was part of the September 2025 cohort at Curious PM, alongside 13 other talented participants.