Data-Driven Entrepreneur Information and Analysis for Business Opportunity Evaluation

We High Situations of request query can make it challenging for entrepreneurs to estimate business openings. Gathering and assaying data through various ways and technologies is increasingly getting a vital system for managing this query in multitudinous entrepreneurial crapshoots. This approach is constantly labeled “data driven entrepreneurship.” We explore a dynamic, data driven strategy to navigate request query in the assessment of business prospects. Our focus is on each individual’s investment portfolio, where each investment not only offers desired profits but also provides certainly about a specific requested parameter for an individual business occasion. We build a data science model that assesses request data (for ex: multimedia, finance, sports etc. …) while considering the investor’s trouble appetite, functional resources, business practices, character, investment, profits, time limits and compliance limitations. Our quantitative results show that rather than simply aiming for the topmost projected returns, entrepreneurs can choose investments that offer accurate information, trouble mitigation, or request influence according to their cash vacuity and trouble forbearance. Thanks to the power of data analysis, entrepreneurs can navigate misgivings more effectively, leading to better informed opinions regarding business openings.

Authors

Publication Details

Published
2024-11-23
DOI
https://doi.org/10.59544/vupj8659/icrcct24p9
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data-Driven Entrepreneur Information and Analysis for Business Opportunity Evaluation

N. Sudhakar Reddy, K. Karthika, M. R., Y. Shi
Big Data and Business Intelligence
article

Data-Driven Entrepreneur Information and Analysis for Business Opportunity Evaluation

N. Sudhakar Reddy, K. Karthika, M. R., Y. Shi
article en

Abstract

We High Situations of request query can make it challenging for entrepreneurs to estimate business openings. Gathering and assaying data through various ways and technologies is increasingly getting a vital system for managing this query in multitudinous entrepreneurial crapshoots. This approach is constantly labeled “data driven entrepreneurship.” We explore a dynamic, data driven strategy to navigate request query in the assessment of business prospects. Our focus is on each individual’s investment portfolio, where each investment not only offers desired profits but also provides certainly about a specific requested parameter for an individual business occasion. We build a data science model that assesses request data (for ex: multimedia, finance, sports etc. …) while considering the investor’s trouble appetite, functional resources, business practices, character, investment, profits, time limits and compliance limitations. Our quantitative results show that rather than simply aiming for the topmost projected returns, entrepreneurs can choose investments that offer accurate information, trouble mitigation, or request influence according to their cash vacuity and trouble forbearance. Thanks to the power of data analysis, entrepreneurs can navigate misgivings more effectively, leading to better informed opinions regarding business openings.

Decent work and economic growth
Openalex Percentile: Top 67%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Data-Driven Entrepreneur Information and Analysis for Business Opportunity Evaluation — N. Sudhakar Reddy, K. Karthika, et al. · (2024) | TGRS Research Map | TGRS