Spandan Borkar

Business Analyst, Data analyst

Talent

Nagpur, NagpurMember since June 2, 2025

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About

Aspiring analyst with a proven ability to extract insights from complex datasets using SQL, Python, Excel, and Power BI, with strong foundations in ETL, data cleaning, transformation, and visualization. Delivered 60% efficiency gains through automated reporting and KPI tracking. Adapt at building interactive dashboards, enabling data-driven decision making and collaboration across cross-functional teams in Agile environments.

Experience

PROFESSIONAL EXPERIENCE

IGT solutions September 2024 – February 2025

Process Analyst Intern

 Applied SERVQUAL model to evaluate inflight services, uncovering 3 key pain points affecting satisfaction scores.

 Conducted primary surveys of 1,500 passengers across multiple cities, collecting structured data on key

KPIs like seat comfort, entertainment, cleanliness, crew responsiveness, and food quality.

 Conducted structured interviews with 10 Air India staff and analyzed qualitative feedback using thematic coding techniques.

 Identified 3 key service pain points (inflight entertainment, cabin cleanliness, and food consistency) contributing to a

20–30% drop in satisfaction scores.

 Delivered a roadmap of 6 strategic recommendations, including IFE upgrades, digital complaint tracking,

and menu customization options that were acknowledged by internal management and integrated into the Agile planning cycle.

 Strengthened data-driven decision-making framework and contributed to enhanced customer experience benchmarking efforts.

RELEVANT PROJECTS

Retail Sales Dashboard (Superstore)

Tools: Excel, Power BI, DAX

 Developed a data model and interactive dashboard from 12,000+ records, revealing profit margin trends and inventory misalignments, leading to a 15% inventory cost reduction.

 Used DAX to calculate key KPIs including Gross Margin %, YOY growth, and Product Performance Index.

Sales Forecasting with Time Series

Tools: Python, Power BI, Excel

 Forecasted monthly revenue trends enabling demand planning for a retail client via time-series modeling and

ETL pipelines.

 Reduced stockouts by 18% and improved procurement decisions with visualization ofseasonal patterns.