Subham Baral

Strategic data analyst skilled in distilling and interpreting large data sets to drive informed decision-making. Proven ability to develop and deliver compelling presentations that effectively communicate data insights. Expertise in dashboard design and data collection, fostering collaboration across teams to enhance analytical processes. Seeking to leverage analytical skills in a dynamic environment to optimize data-driven strategies.

Young Leadership Program, IIM Raipur

• Painting
• Photography
• Drama Writing
• Drama direction
• Acting

Analyze customer buying patterns to identify and define customer segments, using clustering techniques Discover association rules among line items to create a targeted customer base Develop and execute a campaign for this target base, and subsequently evaluate its effectiveness Finally, implement strategies to retain these customers and mitigate churn.

• Data-driven approach: Using techniques such as clustering and association rule mining

• Customer segmentation: Dividing customers into distinct groups based on shared characteristics

Targeted marketing: Creating campaigns specifically for identified segments

Campaign evaluation: Assessing the impact of the campaign on customer behavior and business objectives

• Customer retention: Implementing strategies to prevent customer churn, and maintain long-term relationships

I analyze vast historical datasets to determine the optimal quantity of materials required for specific jobs, ensuring accurate forecasting and resource allocation. Leveraging advanced statistical methods, I establish benchmarks to identify anomalies in material usage, enabling proactive theft prevention, and efficient inventory management

To achieve this, I design and implement complex data models that integrate multiple data sources, transforming raw data into meaningful insights I develop sophisticated dashboards in Power BI, incorporating interactive visualizations, and advanced analytics to provide real-time monitoring and trend analysis My expertise in handling large datasets, optimizing data processing, and structuring complex reports allows stakeholders to make data-driven decisions with confidence, enhancing overall operational efficiency

The primary objective of this project is to automate the data extraction process from the Coal India website, ensuring seamless data storage, efficient analysis, and accurate reporting By eliminating manual efforts, this automation aims to reduce errors, improve data accessibility, and enhance decision-making in tracking critical business metrics, such as coal production, pricing trends, and tender details To achieve this, Automation Anywhere will be utilized to develop a robust bot capable of autonomously navigating the website and extracting relevant data points The bot will be designed to efficiently locate and retrieve key information, such as coal production figures, pricing details, and tender updates, using advanced web scraping techniques to ensure smooth interaction with dynamic web elements Exception handling mechanisms will be implemented to account for potential changes in the website structure, ensuring continuous data extraction without failures Once extracted, the data will undergo cleaning, validation, and structuring before being stored in a centralized database or cloud repository for real-time availability Automated workflows will be established to ensure that data is processed and updated at predefined intervals, reducing the need for manual intervention Furthermore, the extracted data will be seamlessly integrated into Power BI dashboards, allowing for comprehensive visualization, and in-depth analysis These dashboards will provide stakeholders with real-time insights into market trends, production patterns, and pricing fluctuations Additionally, automated alerts and reports will be configured to notify users of critical changes or anomalies in the data, ensuring timely and informed decision-making By implementing this end-to-end automation solution, businesses can significantly enhance operational efficiency, optimize resource management, and improve overall business intelligence capabilities.

As a dashboard developer, I design and implement a predictive analytics dashboard using Power BI, BigQuery, ML, and LLMs to forecast solar panel manufacturing component prices for data-driven decision-making. Handling large, complex datasets, I build an optimized data model that integrates historical pricing, market trends, and supply chain data. Using BigQuery for efficient data processing, ML algorithms to detect pricing patterns, and LLMs to extract insights, I enhance predictive accuracy. The Power BI dashboard features dynamic visualizations, real-time updates, and advanced analytics like time-series forecasting and anomaly detection. To ensure seamless performance, I implement query optimizations and caching while focusing on UX design for intuitive navigation. This solution empowers businesses to optimize procurement, reduce costs, and improve efficiency with accurate, actionable insights.

As a dashboard developer, I design and implement a predictive analytics dashboard using Power BI, BigQuery, ML, and LLMs to forecast the prices of solar ancillary components, enabling data-driven decision-making. Working with large and complex datasets, I build an optimized data model that integrates historical pricing, market trends, raw material costs, and supply chain data. Leveraging BigQuery for efficient data processing, ML algorithms to identify pricing patterns, and LLMs to extract insights from industry reports, I enhance predictive accuracy. The Power BI dashboard features dynamic visualizations, real-time updates, and advanced analytics like time-series forecasting and anomaly detection, helping stakeholders anticipate market shifts. To ensure smooth performance, I implement query optimizations and caching, while focusing on UX design for intuitive navigation. This solution empowers businesses to optimize procurement, reduce costs, and improve operational efficiency with accurate, actionable insights into solar ancillary component pricing.

As a dashboard developer, I design and implement a pricing intelligence dashboard using Power BI, SQL, ML, and LLMs to analyze and forecast steel prices, enabling data-driven decision-making. Working with large and complex datasets, I build an optimized data model that integrates historical pricing, raw material costs, market trends, demand-supply dynamics, and global economic indicators. Using SQL as the primary data storage and processing engine, I ensure efficient data handling, scalability, and seamless integration with analytical tools. ML algorithms detect pricing patterns, while LLMs extract insights from industry reports and financial news, enhancing predictive accuracy. The Power BI dashboard features dynamic visualizations, real-time updates, and advanced analytics like time-series forecasting and anomaly detection, allowing stakeholders to monitor price fluctuations and anticipate market shifts. To maintain seamless performance, I implement query optimizations, indexing, and caching strategies, while focusing on UX design for intuitive navigation. This solution empowers businesses to optimize procurement, reduce costs, mitigate risks, and make informed decisions with accurate, actionable insights into steel pricing trends.

As a dashboard developer, I design and implement the AEML Material Forecasting Dashboard using Power BI, SQL, and Power BI Dataflows to analyze and predict material requirements, ensuring efficient procurement and inventory management. Working with large and complex datasets, I develop an optimized data model that integrates historical material consumption, procurement trends, supplier data, and demand forecasts. Power BI Dataflows serve as the primary data ingestion layer, enabling seamless data transformation and integration, while SQL acts as the backend database for structured storage, query optimization, and performance tuning. Leveraging ML algorithms, I identify usage patterns, detect anomalies, and enhance forecast accuracy. The Power BI dashboard features interactive visualizations, real-time updates, and advanced analytics like time-series forecasting and trend analysis, empowering stakeholders to make proactive decisions. By implementing query optimizations, indexing, and caching strategies, I ensure smooth performance and scalability. This solution helps AEML streamline material planning, reduce costs, prevent shortages, and improve operational efficiency through accurate, data-driven insights.

As a dashboard developer, I design and implement the Power Price Forecasting Dashboard using Power BI and SQL to analyze and predict electricity prices based on demand and supply dynamics, enabling data-driven decision-making. Working with large and complex datasets, I develop an optimized data model that integrates historical power consumption, generation capacity, market demand, fuel costs, weather patterns, and regulatory factors. SQL serves as the backend database, ensuring efficient data storage, query performance, and seamless integration with analytical tools. Leveraging ML algorithms, I identify demand-supply trends, detect anomalies, and enhance price forecast accuracy. The Power BI dashboard features interactive visualizations, real-time updates, and advanced analytics like time-series forecasting, demand-supply correlation, and anomaly detection, allowing stakeholders to monitor price fluctuations and anticipate market changes. To ensure smooth performance, I implement query optimizations, indexing, and caching strategies, while focusing on UX design for intuitive navigation. This solution empowers businesses and energy planners to optimize pricing strategies, manage risks, improve energy procurement, and enhance operational efficiency through accurate, data-driven insights.

As a dashboard developer, I design and implement the APM to Distribution Transformer (DT) – Consumer to DT Tagging Dashboard using Power BI and SQL to track and update consumer connections to distribution transformers (DTs) on a daily basis. Working with large and dynamic datasets, I develop an optimized data model that integrates consumer records, transformer load data, geospatial mapping, and real-time power distribution metrics. SQL serves as the backend database, ensuring efficient data storage, query performance, and seamless data integration. Power BI processes and visualizes this data, enabling stakeholders to monitor daily changes, analyze load distribution, and identify discrepancies in tagging. The dashboard features interactive visualizations, real-time updates, and advanced analytics, including automated anomaly detection, geospatial mapping of DTs and consumers, and predictive analytics for load forecasting. To enhance performance, I implement query optimizations, indexing, and caching strategies, ensuring smooth and scalable data processing. This solution empowers utility companies and grid operators to improve network reliability, optimize transformer utilization, reduce losses, and enhance operational efficiency with accurate, real-time insights into consumer-to-DT tagging.

As a dashboard developer, I design and implement a Sales Reporting Dashboard for a renowned beer manufacturing company using Power BI and SQL to track and analyze sales performance across various regions, products, and distribution channels. Working with large and complex datasets, I develop an optimized data model that integrates historical sales data, market demand, pricing trends, distributor performance, and customer preferences. SQL serves as the backend database, ensuring efficient data storage, query optimization, and seamless integration with Power BI. The dashboard provides real-time insights with interactive visualizations, including sales trends, regional performance breakdowns, top-selling products, revenue comparisons, and seasonal demand analysis. Advanced analytics such as forecasting, anomaly detection, and profitability analysis enable stakeholders to make data-driven decisions. By implementing query optimizations, indexing, and caching strategies, I ensure smooth performance and scalability. This solution empowers sales teams, marketers, and executives to track KPIs, identify growth opportunities, optimize pricing strategies, and enhance overall sales performance through accurate, real-time insights into the beer industry’s dynamic market.

As a UI Designer and Dashboard Developer, I design and develop a Power Intelligence Platform using Power BI, creating intuitive and visually compelling dashboards that transform complex data into actionable insights. Working with large and diverse datasets, I craft an optimized data model that integrates real-time power consumption, generation analytics, grid performance, market trends, and predictive insights. My role involves UI/UX design, ensuring that dashboards are user-friendly, interactive, and visually engaging while maintaining clarity and efficiency. Using Power BI's advanced features, I develop custom visuals, dynamic filtering, real-time updates, and AI-powered analytics, enabling seamless exploration of energy data. I focus on color psychology, layout optimization, and intuitive navigation, ensuring users can easily interpret key performance indicators (KPIs), trends, and anomalies. By implementing DAX calculations, query optimizations, and performance tuning, I ensure a smooth and scalable experience. This Power Intelligence Platform empowers energy analysts, decision-makers, and businesses to optimize power usage, predict demand, reduce costs, and enhance grid efficiency with accurate, real-time, and visually compelling insights.