Data-Driven Decision Making How Small is a B.Tech project topic for Information Technology. It gives students a clear starting point for research, implementation planning, and documentation.
Data-Driven Decision Making How Small Project Details
| Abstract |
Small and medium‑sized enterprises (SMEs) in the United States face tough competition and often have limited money and staff. Large corporations have long led the way in using advanced data analytics, but recent technology advances let smaller firms use Business Intelligence (BI) tools, Machine Learning (ML) algorithms, and modern Customer Relationship Management (CRM) systems. This project offers hands‑on help and a clear plan for building a low‑cost, scalable data‑analytics pipeline that fits small businesses. By combining open‑source BI platforms with predictive ML models, the framework lets owners do sales forecasting, customer segmentation, and risk assessment. The approach focuses on three steps: * Collecting data from the various points where a
small business interacts with customers and operations. * Cleaning and preparing both structured and semi‑structured data. * Creating visual reports that can be acted on. Following this method, small firms can move from gut‑feel decisions to data‑driven strategies. That shift helps them allocate resources better and lower operational risks. The architecture described here can be used as a practical guide for both academic research and real‑world implementation in applied business analytics.
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| Reference Paper |
Data-Driven Decision Making: How Small Businesses Can Leverage Analytics to Scale in the United States |
| Domain |
Data Analytics |
| Sub-Domain |
Data Analytics |
| PDF Download |
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| Get Help |
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