AROhI: An Interactive Tool for Estimating ROI of Data Analytics

TL;DR

This paper introduces AROhI, an interactive tool that leverages advanced machine learning techniques to estimate the ROI of data analytics projects, aiding decision-making on technology investments.

Contribution

The work presents a comprehensive, ML-based tool that automates dependency extraction and ROI analysis, integrating active learning, transfer learning, and BERT for improved accuracy.

Findings

Demonstrates the use of advanced ML for dependency extraction

Provides a mechanism to compute ROI of ML algorithms

Shows trade-offs between cost and benefits of analytics investments

Abstract

The cost of adopting new technology is rarely analyzed and discussed, while it is vital for many software companies worldwide. Thus, it is crucial to consider Return On Investment (ROI) when performing data analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide decision support on the What?, How?, and How Much? Analytics for a given problem. This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using requirements dependency extraction and their ROI analysis as use case. Utilizing advanced ML techniques such as Active Learning, Transfer Learning and primitive Large language model: BERT (Bidirectional Encoder Representations from Transformers) as its various components for automating dependency extraction, the tool outcomes demonstrate a mechanism to compute the ROI of ML algorithms to present a clear picture of trade-offs between the cost and benefits of a technology investment.