Industry Role
Business Development
Nishitosh Khod
I work on the business and go to market side of Data, ML and AI projects, helping companies identify the right problems and convert them into executable initiatives.
My background is in digital marketing, which helps me understand how businesses think about growth, ROI and decision making. I use this perspective to frame IT projects around real outcomes, not just technical delivery.
In practice, my role involves:
- Working with founders and leadership teams to identify data, ML and AI use cases - Translating business requirements into clear project scopes for delivery teams - Supporting data engineering and AI initiatives from discovery to early execution
I am not a hands on engineer. I work closely with technical teams to ensure projects are commercially sound, correctly scoped, and aligned with business priorities.
The focus is simple: Clear problems Clear ownership Low risk execution
Articles by Nishitosh
Why Accurate Manufacturing Data Still Destroys Production Decisions
Your production dashboard shows everything is under control - inventory stable, utilization strong, targets achievable. Then production collapses. The numbers were technically correct. The decisions were still wrong. Here's why manufacturing data accuracy doesn't guarantee operational success.
Why Manufacturing Data Looks Accurate but Still Breaks Production Decisions
Manufacturing data can be accurate yet still cause failed production decisions. Learn why dashboards mislead planners and how better data modeling fixes firefighting on the shop floor.
How Data Moves Inside a Company From Source to Decision
Most founders think dashboards equal clarity. They don't. Dashboards only display what reaches them and if your data flow is broken, your decisions are wrong even when the charts look clean.
Data Engineering vs Data Analytics: The Real Difference in Business Terms
Most businesses confuse data engineering and data analytics, then expect analytics to fix problems that only engineering can solve. The result is wasted spend, wrong hires and broken expectations.
Why Most Businesses Do Not Need Heavy Data Engineering
Most small and mid-sized businesses overspend on heavy data engineering without improving decisions. Learn when it helps, when it hurts, and what to fix first.
What Is Data Engineering for Business? (Do You Actually Need It)
If your team is fixing numbers more than using them, you don't have a reporting problem, you have a data engineering problem. Learn what data engineering actually means for business, what it costs to ignore it and how to know if you truly need it right now.