Revenue analysis dashboard working with a fragmented, real-world style dataset from a fireworks company.

Built this revenue analysis dashboard as part of an Information Systems assignment working with a fragmented, real-world style dataset from a fireworks company.
The data was intentionally messy: no direct revenue at transaction level, weak links between entities, and incomplete inventory tracking. Instead of forcing clean models, I worked with what was available deriving revenue and profit from pricing logic, matching products by year, and using stock shortfall as a proxy for demand pressure.
That approach led to a few useful insights:
• Demand vs shortfall analysis highlighted where stockouts are limiting revenue, not demand.
• Revenue and traffic trends showed repeat spike–drop cycles, pointing to supply or capacity constraints.
• Store-level data made it clear that demand is geographically concentrated, not customer-driven.
• Salesperson vs commission patterns suggested incentives are not aligned with performance.
• Inventory views helped separate high-velocity products from slow-moving stock despite missing stock flow data.
What I took from this assignment: clean insights don’t require clean data. With the right assumptions and structure, even incomplete datasets can surface clear business decisions especially around expansion, inventory prioritisation, and operational bottlenecks.

 https://www.linkedin.com/posts/anshulmahesh_informationsystems-dataanalytics-businessintelligence-ugcPost-7455914434704818177-1SEo/?utm_source=share&utm_medium=member_desktop&rcm=ACoAADQHJjABBbhkyzt5wOJs1C3_dyokOEH7lGM

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